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2012 Indirect Benefits of Women’s Education: Evidence from Bangladesh Selina Akhter University of Wollongong
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Thesis
Indirect Benefits of Women’s Education: Evidence from Bangladesh
A Thesis Submitted in the Fulfillment of the Requirement for the Award of the Degree Doctor of Philosophy
University of Wollongong School of Economics and Information Systems Faculty of Commerce New South Wales, Australia
Selina Akhter Deputy Chief Bangladesh Planning Commission
Copyright © Selina Akhter
Declaration
I hereby declare that the material contained within the thesis has not previously been submitted for the award of any other degree or diploma at this University or any other institutes of learning.
I certify that any help received in preparing this thesis and all sources are acknowledged.
Selina Akhter Deputy Chief Bangladesh Planning Commission
i
Acknowledgement
It is a great opportunity for me to acknowledge people who helped me in various ways in the long way of doing Ph.D. First, I would like to express my profound gratitude to my principal supervisor Ann T. Hodgkinson, Associate Professor, Department of Economics, University of Wollongong, Australia, from whom I received encouragement, guidance, detailed criticism and insightful suggestions which helped me focus my ideas and enriching thinking, and finally shaped the thesis. I also express my gratitude to Dr. Khorshed Chowdhury, School of Economics, University of Wollongong, Australia for his kind and amiable assistance.
It is my immense pleasure to work with Richard Palmer-Jones, Senior Lecturer, University of East Anglia, England, while working on Bangladesh Planning Commission - the working place of mine, who helped me enormously by his own way of teaching especially technical aspects of data analysis of this thesis. I express my special thanks to him. I am especially grateful to my senior colleague Mr. A.K.M. Khorshed Alam who read the manuscript and helped me to improve it. I am very grateful to authority of University of Wollongong, Australia for providing me financial assistance without which it would have been impossible to complete the thesis successfully. I offer special thanks to my husband who accompanied me with all sorrows and happiness in managing familial issues during the long period of study. I also offer heartfelt love and affection to my sweet daughters for their sacrifices and pains undertaken. I pay homage and regards to my lovely parents especially my father who inspired me mentally and spiritually to continue study. Finally, I would like to convey my thanks to office colleagues with whom I shared the difficulties due to study.
Selina Akhter Deputy Chief
Bangladesh Planning Commission
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Contents Page no. Declaration i Acknowledgement ii Table of Contents iii-ix Abstract x Chapter 1
Introduction 1-20
1.1 Geographical Location of Bangladesh 1-5
1.1.1 Population 2-3 1.1.2 Education 3 1.1.3 Health and Nutrition 3-5 1.2 Socio-economic Context: Present Status, Problems and Prospects 5-10
1.2.1 Background 5-6 1.2.2 Development Plans and Programs 6 1.2.3 Poverty Trend and Poverty Reduction Initiatives 7-8 1.2.4 Nutritional Status and Policies 8-9 1.2.5 Educational Attainment and Policies 9-10 1.3 Research Problem 10-12 1.3.1 Need for Research in Women’s Education 12 1.4 Objectives of the Study 12-13 1.5 Theoretical Basis 13-15 1.6 Methodology 15-16 1.7 Structure of the Thesis 16-18 1.8 Definitions of Terms 18-20
Chapter 2
The Benefits from Educating Women: A Literature Review 21-65
2.1 Human Capital Theory 23-33 2.1.1 The Rate of Returns to Education 25-28 2.1.2 Determinants of Human Capital 28-31 2.1.3 Application of Human Capital Theory to Women’s Education 31-33
iii 2.2 Benefits of Women’s Education 33-44 2.2.1 Economic Benefits 33-38 I. Economic Growth 33-34 II. Women’s Labour Force Participation and the Rate of Returns to 34-37 Education III. Wage Discrimination 37-38 2.2.2 Social Benefits 38-44 I. Fertility and Women’s Education 38-40 II. Child Mortality and Women’s Education 40-44 2.3 Costs of Children’s Education 44-47 2.3.1 Educational Costs by Gender 44-47 2.4 Women’s Education and Children’s Educational Attainment 47-48 2.5 Women’s Education and Child Nutrition 48-50 2.6 Factors Associated with Lower Women’s Education 50-55 2.7 Consequences of Women’s Lower Level Education 55-57 2.8 Primary Education versus Post-primary Education 57-62 2.9 Concluding Remarks 62-65
Chapter 3
Education Status of Women in Bangladesh 66-102
3.1 Education System in Bangladesh 67-73 3.1.1 General Education 67-70 I. Primary School (Grades I-V) 67-68 II. Secondary School (Grades VI-XII) 68-69 III. Tertiary Level (Grade XII and above) 69-71 3.1.2 Madrasa Education 71-72 3.1.3 Technical and Vocational Education 72 3.1.4 Non-formal Education 72-73 3.2 Education Policies and Strategies 73-80 3.2.1 Major Policies regarding Education 74 3.2.2 Strategies Regarding Education 74-75 3.2.3 Programs Implemented in Education Sector 75-79 I. Stipend Program for Primary School Students 76 II. Primary Education Development Program 76 III. Reaching out of School Children Project 76-77 IV. School Feeding Program 77 iv V. Female Secondary School Stipend Program 77 VI. Secondary School Sector Improvement Program 77-78 VII. Compulsory Primary Education Implementation Monitoring 78 Unit VIII. Total Literacy Movement 78-79 3.2.4 Financial Expenditure 79-80
3.3 Achievements at Various Education Levels 80-85 3.3.1 Primary Education 81-82 3.3.2 Secondary Education 82-83 3.3.3 Tertiary Education 83-84 3.3.4 Adult Literacy Rate 84-85 3.4 Constraints and Challenges in Education Sector 86-91 3.4.1 ‘Out of Reach’ School and Dropped out Children 86-87 3.4.2 Quality of Education 87-88 3.4.3 Rural-Urban Difference 88 3.4.4 Educational Expenses 88-89 3.4.5 Poor Governance 89 3.4.6 Lack of Resources 89-91 3.5 Overall Women’s Status in Bangladesh 91-96 3.6 Comparison among South Asian Countries 96-100 3.7 Concluding Remarks 100-102
Chapter 4
Health Status of Women in Bangladesh 103-133
4.1 Health System in Bangladesh 104-108 4.1.1 Health Infrastructure and Services 105-108 I. Primary Health Care Services 105-106 II. Secondary Health Services 106 III. Tertiary Health Care 106-108 4.2 Health Policies and Strategies 108-115 4.2.1 Health Policies and Strategies 108-110 4.2.2 Health Sector Program 110-112 I. National Nutrition Program 111 II. Area Based Community Nutrition Program 111-112 III. School Feeding Program 112
v 4.2.3 Drug Policy 112-113 4.2.4 Population Policy 113-114 4.2.5 Financial Allocation to Health Sector 114-115 4.3 Achievements in Health Issues 115-121 4.3.1 Child Morality Rate 116-117 4.3.2 Maternal Morality Ratio 117-118 4.3.3 Nutritional Status of Children 118-120 4.3.4 Fertility Reduction and Population Growth 120-121
4.4 Constraints and Challenges in Health Sector 122-127
4.4.1 Infant and Child Morality Rate 122 4.4.2 Maternal Morality Ratio 122-123 4.4.3 Nutritional Status of Children 123-125 4.4.4 Rural-Urban Differences 125 4.4.5 Lack of Resources 125-127
4.5 Overall Women’s Status in Health Services 127-128 4.6 Comparison among South Asian Countries 128-131 4.7 Concluding Remarks 131-133
Chapter 5
Methodology 134-173
5.1 Explanation of Variables and Terminologies 137-139 5.2 Justification for Using Quantitative Approach 139-140 5.3 Hypotheses 140-141 5.4 Model Specification 142-158
5.4.1 The Logit and the Probit Models 142-144 I. The Logit Model 142-143 II. The Probit Model 143-144 5.4.2 Model for Educational Attainment 144-150 5.4.3 Multiple Regression Model 151-154 5.4.4 Child Nutrition Model 154-158
5.5 Regression Technique 159-165
5.5.1 Estimation of Children’s School Attendance 159-162 I. Multicollinearity 159-161 II. Selection Bias 161-162 III. Heteroscedasticity 162
vi IV. Discussion of Hypotheses 162 5.5.2 Estimation of Children’s Nutritional Status 162-165 I. Multicollinearity 163-164 II. Heteroscedasticity 164 III. Regarding Hypotheses and Beta Coefficient 164-165
5.6 Sources of Data 165-171
5.6.1 Survey Design 165-169 5.6.2 Limitation of Data 169-171
5.7 Concluding Remarks 172-173
Chapter 6
Educational Attainment: Data Analysis and the Expected Results 174-206
6.1 Description of Variables 175-186
6.1.1 Dependent Variables 175-176 I. School Attendance 175-176 6.1.2 Independent Variables 176-186 I. Household Income 176-177 II. Parent’s Education 177-180 III. Father’s Education* boy and Mother’s Education *girl 180-181 IV. Female Headed Household 181 V. Girls’ Status in School Attendance 181-182 VI. Age of Child 182-183 VII. Average Expenditure in Schooling 183-185 VIII. Distance to School 185 IX. Supply of Electricity 185-186
6.2 Estimated Results on Children’s School Attendance 186-194
6.2.1 Primary School Attendance: National Level 186-188 6.2.2 Primary School Attendance: Rural Level 188-190 6.2.3 Secondary School Attendance: National Level 190-192 6.2.4 Secondary School Attendance: Rural Level 192-194
6.3 Explanation of Estimated Results of Independent Variables 194-204
6.3.1 Effect of Household Income 194-196 I. Primary School Attendance 194-195 II. Secondary School Attendance 195-196
vii 6.3.2 Effects of Parent’s Education 196-199 I. Primary School Attendance 196-197 II. Secondary School Attendance 197-199 6.3.3 Effect of Female Headed Household 199-200 6.3.4 Effect of Girls’ Status in School Attendance 200-201 6.3.5 Effect of Year wise Age of Children 201-202 6.3.6 Effect of Average Expenditure of Schooling 202-203 6.3.7 Effect of Distance to School 203 6.3.8 Effect of Supply of Electricity 203-204
6.4 Explanation of Hypotheses 204-205 6.5 Concluding Remarks 205-206
Chapter 7
Child Nutrition: Data Analysis and the Expected Results 207-234
7.1 Description of Variables 208-215
7.1.1 Dependent Variables 208-210 I. Child Nutrition (Stunting and Underweight) 208-210 7.1.2 Independent Variables 210-215 I. Daily per capita Calorie Consumption 210-211 II. Parent’s Education 211-212 III. Girls’ Status in Child Nutrition 212 IV. Sources of Drinking Water 212-213 V. Types of Toilet Used by the Household Members 213 VI. Washing Mother’s Hand 214 VII. Distance to Health Centers 214-215 7.2 Estimated Result on Child Nutrition 215-218
7.2.1 Effects on Stunting: National Level 215-217 7.2.2 Effects on Underweight: National Level 217-218 7.3 Estimated Results on Child Nutrition: Rural Area 219-222
7.3.1 Effects on Stunting: Rural Area 219-221 7.3.2 Effects on Underweight: Rural Area 221-222 7.4 Explanation of Estimated Result of Independent Variables 223-232 7.4.1 Effect of Daily per capita Calorie Consumption 223 7.4.2 Effect of Parent’s Education 223-229 I. Father’s and Mother’s Education: Comparative Effect on 224-226 viii Stunting II. Father’s and Mother’s Education: Comparative Effect on 226-229 Underweight 7.4.3 Effect of Girls’ Status in Child Nutrition 229 7.4.4 Effect of Sources of Drinking Water 229-230 7.4.5 Effect of Types of Toilet used by the Household Members 230-231 7.4.6 Effect of Washing Mother’s Hand 231 7.4.7 Effect of Distance to Health Centers 231-232 7.5 Explanation of Hypotheses 232-233 7.6 Concluding Remarks 233-234 Chapter 8
Conclusion 235-251
8.1 Objectives and the Research Process 235-236 8.2 Research Questions and the Hypotheses 237-238 8.3 Major Findings Obtained from the Analysis 238-245
8.3.1 Major Findings: School Attendance 238-242 8.3.2 Major Findings: Child Nutrition 242-245 8.4 Policy Implications of this Study 245-247
8.4.1 Policy Implications: School Attendance 245-246 8.4.2 Policy Implications: Child Nutrition 246-247 8.5 Contributions of the Study and Areas of Research 247-251
8.5.1 An Econometric Framework for Empirical Analysis 248-249 8.5.2 First Study in the Bangladeshi Context 249 8.5.3 Methodological Contribution 249-250 8.5.4 Suggestion for Future Studies 250-251
References 252-269
Appendices 270-296 1 Appendix A - Tables 270-281 2 Annexure B5 - Correlation Tables 282-285 3 Appendix C - Figures 286 4 Appendix D - Definitions 287-291 5 Appendix E - Abbreviations 292-296
ix
Abstract Education, although contributes significantly to economic growth through human resource development, women education deserves attention for generating substantial benefits for the society by educating themselves as well as rearing healthy and better educated children as a potential workforce. However, the benefits of women’s education are commonly measured by increased productivity through enhanced labour market participation. In many developing countries, in fact, a non-linear relationship between women education and labour market participation is observed very often. Thus, studies do not always support the simple intuitive argument that increasing expenditure on women's education enhances economic development in such countries. This contradictory effect is possibly due to the fact that more educated women may live in higher income households, which can support their withdrawal from the workplace as primary care givers to the children. This effect may be stronger in some countries on account of traditional and cultural attitudes. However, it is argued in this thesis that educated women, by playing their role as mothers, indirectly contribute to the economic prosperity of their country. Educated women improve their own life as well as their children’s health by being able to keep the family size small and by utilizing their knowledge to implement effective health, hygienic and nutritional practices at home. Importantly, they also invest more in acquiring children’s education. Thus, even if educated women do not participate in the labour market, they indirectly generate longer term economic benefits. These indirect economic benefits are often overlooked in the debate of benefits ensuing from higher female education particularly in developing countries. In persuasion of the relationships between mother's education and children’s school attendance as well as nutritional status in terms of being stunted or underweight, this thesis considers the socio-economic context and the household level data of Bangladesh. The empirical investigation through multiple regression models shows that child nutrition is unambiguously improved by mother’s education holding all other variables constant, father’s education in particular. By contrast, the argument is not sufficiently supported by the statistical analysis using probit model rather both father’s and mother’s education equally play an important role in acquiring children's education.
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Chapter 1
Introduction
1.1 Geographical Location of Bangladesh
Bangladesh is a small country located in South Asia and covers an area of 144,598 square kilometres. It is almost entirely surrounded by India with a southern deltaic coastline on the Bay of Bengal. The most significant feature of the landscape is the extensive network of large and small rivers which are essential to the socio-economic life of the nation (NIPORT 2005). The climate of Bangladesh is dominated by seasonal monsoons. For administrative purposes, the country is divided into 6 divisions, 64 districts and 507 thanas (sub-districts) (BBS 2006b).
Bangladesh has become an independent nation in 1971 after a nine month long liberation war with its counterpart West Pakistan. After independence, the country inherits a war trodden economy based largely on agriculture sector. It also inherits around seventy and half million people, acute shortage of skilled labour force, lack of employment opportunities and severe poverty among the common people. In this context, the then government emphasized social sector programs such as population control, social safety net programs and human resource development and also emphasized infrastructure development projects. Although, the country had to struggle with various difficulties since its inception, it maintained an economic growth of around five per cent during the nineties. It gained on average 6 per cent growth per year since 2000 to till date and per capita income in Bangladesh was around US$ 621 in 2008 (GoB 2009a). Despite this achievement, the reality is more than one third of its population live below the poverty line.1
1 Poverty is defined if any one consumes below the threshold level of 2122 kilo calorie per day. This is also known as upper poverty line while lower poverty line is defined as if daily calorie intake is below the level of 1805 kilo calorie per day per person. These lines are measured by Bangladesh Bureau of Statistics based on “Household Income and Expenditure Survey” conducted at every five year interval.
1 1.1.1 Population
Bangladesh is a densely populated country with a population of around 160 million (World Development Indicators 2011). The average population density is more than 900 persons per square kilometre. The annual population growth rate declined from 2.33 per cent in 1981 to 2.15 per cent in 1991 (BBS 2005) and further to 1.26 per cent in 2008 (GoB 2010a). The total fertility rate fell by a half from 5.24 per cent to 2.30 per cent between 1981 and 2008. This constitutes a significant achievement towards demographic transition. The distribution of population by age group is shown in Table 1.1.
Table 1.1: Distribution of Population by 5-year age groups in 2007 (in %)
Age group (years) Both Male Female 0-14 36.3 37.5 35.3 15-19 10.8 9.8 12.0 20-24 9.1 7.7 10.3 25-29 7.9 7.5 8.2 30-34 6.4 6.1 6.7 35-39 6.5 6.6 6.4 40-44 5.3 5.7 4.9 45-49 4.5 4.8 4.3 50-54 3.7 4.4 2.9 55-59 2.4 2.1 2.7 60+ 7.1 8.0 6.5 Total 100 100 100
Source: Bangladesh Demographic and Health Survey 2007, National Institute of Population Research and Training (NIPORT), Bangladesh, 2008.
From Table 1.1, it is seen that population is characterised by a high proportion of young age population below 15 years (36.3 per cent). The proportion of reproductive women (15-49 years) is nearly 53 per cent of all women, which indicates the high growth potential of future population as well as high fertility situation. Population of aged above 60 years represents only 7 per cent of total population. Due to relatively high fertility rate and high young age-structure, population will continue to grow rapidly until population stabilization will be taken place, which is anticipated by the year 2050 (GoB 2005b; NIPORT 2008). However, Bangladesh is a country in the world with high density of population.
2 1.1.2 Education The adult (15 years and above) literacy rate at national level was 58 per cent in 2008 and the rate was 63 per cent for males as against 49 per cent for females. Net enrolment rate (NER) in primary school was 91 per cent in 2008. Importantly, there is no difference in primary enrolment rate between boys and girls rather in many cases, girls exceeds boys‟. The ratio of girls to boys at primary school participation was 51:49 and the ratio at secondary level was 50:50 (BANBEIS 2009) in 2008. These achievements are gained due to various policies such as stipend program particularly for girls‟ up to higher secondary level, tuition fee free schooling for all boys and girls at primary school, distribution of free text books for all boys and girls up to secondary level - were undertaken and being implemented successfully by the governments of Bangladesh. However, despite these achievements, the country faces serious challenges in achieving completion rate for five-year cycle of primary school, which is around 50-52 per cent during the last few years. At the secondary level, though enrolment of girls increased compared to boys, girls drop out rate is higher than boys and very few female students can continue their education after secondary level. Consequently, gender disparity is quite significant as females are only one fourth of the total students at the tertiary level. However, the literacy rate in Bangladesh is still below than the international standards and also the quality of education remains unexpectedly poor.
1.1.3 Health and Nutrition Bangladesh has achieved an impressive progress in increasing life expectancy and reducing infant mortality rate. The average life expectancy at birth increased from 56 years in 1990 to 66.7 years in 2008 (GoB 2009a). The infant mortality rate has reduced significantly from 94 (per thousand live births) in 1990 to 41 in 2008 (GoB 2010a). In the case of child mortality, a steady decline is observed during the same period. The maternal mortality rate (per 100,000 live births) has reduced from 554 in 1990 to 351 in 2008, which is still one of the highest in the world (GoB 2009c). Increase in contraceptive use is closely related to the decline in total fertility rate and the use of contraception increased to 52.6 percent in 2008 (GoB 2010a). Despite these achievements, availability of health services is extremely limited, particularly in the rural Bangladesh (GoB 2010b).
3 In the case of nutrition, although the average calorie intake per person has improved during the 1990s, the nutritional gain particularly for children is rather stagnant since 2000. Available sources2 of data show that around 50 per cent of Bangladeshi children suffer from either moderate or severe stunting (low height for age) and underweight (low weight for age), which makes it one of the most severe cases of malnutrition in the world (World Bank 2005; NIPORT 2008). According to the BDHS 2007, the percentage of underweight children reduced from 48 per cent in 2000 to 46 per cent in 2007, only a two percentage point decrease (NIPORT 2008). This indicates that the nutritional status has not been improved since 2000. Recognising malnutrition as one the major development problems, the government of Bangladesh has implemented the national nutrition program (NNP) for the period of 2003-2011 aiming to improve the nutritional status of the vulnerable groups, particularly women and children, through providing nutritional food supplement and behavioural change communication (GoB 2009a). The program was extended up to the period of 2011-2016. Bangladesh also faces challenges having a very big size of population. Population growth, although declined noticeably but due to relatively high young age-structure, its population will continue to grow rapidly for years. This enlarged population may aggravate the situation as it already putting tremendous pressure on the country‟s natural resources and leading to severe land shortage. Although in the education field, Bangladesh has made significant progress in primary and secondary school enrolment with gender equality, these apparent successes, however, are matched with the grim realities that around 50 per cent of its total population aged 15 years and above can neither read nor write at all. In addition, the prevailing severity of stunting and underweight inhibit the potential productivity of Bangladeshi children. In this context, with a very limited natural resource base and the abundant human resources, Bangladesh is a unique case - why an investigation of human capital development is so important. Investment in girls‟ education is expected to improve the productivity of future workforce by accumulating knowledge and skills and ensuring higher nutritional gains for their children. Human capital theory supports this assertion
2 Bangladesh Bureau of Statistics (BBS) 2007, Child and Mother Nutrition Survey 2005, BBS. National Institute of Population, Research and Training (NIPORT) 2008, The Bangladesh Demography and Health Survey, 2007 Ministry of Health and Family Welfare, NIPORT.
4 that nurturing the workforce of a country through providing nutrition as well as education will increase their productivity and contribute extensively to the economic growth (Becker 1962; W. Schultz 1961).
1.2 Socio-economic Context: Present Status, Problems and Prospects
1.2.1 Background Agriculture is the dominant sector of Bangladesh‟s economy. It accounts for more than 48 percent of the total employed persons and supports around 75 percent of the total population. Moreover, it contributes around 22 percent to gross domestic product (GDP). Industry is becoming an important sector as a result of expansion of various sub-sectors of manufacturing such as electronic goods, leather products, readymade garment and knitwear. The service sector has also been growing at an average more than 4 percent per year (GoB 2009b). Economic growth in Bangladesh had been around 5 percent per year with a 3.3 percent growth per anum in per capita GDP during the 1990s (BBS 2008). Significant growth in agriculture, industry and service sector contributed to accelerating GDP growth since the 1990s and reach on average 6 percent per year during 2000 onward. In 2008, per capita GDP was around US$ 621 (GoB 2009a). Stable macroeconomic management, government reform programs, expansion of export-oriented manufacturing industries and increase in gross domestic investment are the major factors contributing to economic growth as well as overall macroeconomic development of the country (Ali, Islam & Kuddus 1996; GoB 2008c). Bangladeshi overseas workers generate the country‟s major share of foreign exchange earnings through remittances while the readymade garment and knitwear industry remains the largest export earning source during the last two decades (GoB 2009b). The country has been able to provide jobs for about one million new entrants to the workforce in every year since 1990-91. According to the Labour Force Survey 2005-06, 52 per cent of the employed population are engaged in the non- agricultural sector. In rural areas, agriculture remains the primary source of employment while non-farm activities such as manufacturing, trade, transport and community services are the main sources of employment for 40 per cent of the labour force (BBS 2007c). The service sector has been the main source of providing new jobs (GoB 2009b). However,
5 unemployment remains a daunting challenge for the country to accommodate its rapidly increasing labour force. With the moderate rate of economic growth around 5-6 per cent per year, Bangladesh‟s economy cannot absorb all of its new entrants in the labour force every year. A large number of people remain either unemployed or underemployed and thus, they have no regular income or little income for survival. The country faces the reality of having more than one third of its citizens live in deprivation. Among the poor, significant portions are caught in extreme poverty as measured by food consumption and other basic needs (BBS 2007a).
1.2.2 Development Plans and Programs
Bangladesh has implemented a number of development plans and strategies in combination with rigorous population programs and poverty reduction initiatives since its independence in 1971. Development of human resources through investments in education, health and nutrition is regarded as the main pillars of economic development by the governments of Bangladesh (GoB 2008b). As a signatory of UN Millennium Summit in 2000, Bangladesh is committed to achieving Millennium Development Goals (MDGs) by 2015 (Global MDGs are shown in Appendix Table A5.3). National goals and targets were set in accordance with the goals set at the global level by the United Nations (National MDGs are shown in appendix Table A3.3). The Government of Bangladesh has been implemented two successive national documents „Unlocking the Potential: National Strategy for Accelerated Poverty Reduction (NSAPR I)‟ 2004/05-2007/08 in 2005 and „Moving Ahead: National Strategy for Accelerated Poverty Reduction (NSAPR II)‟ 2008/09 - 2010/11 in 2008. Additionally, in order to reduce regional disparity „A Strategy for Poverty Reduction in the Lagging Regions of Bangladesh‟ in 2008 was formulated (GoB 2008a). Nonetheless, the country is still struggling with a number of development challenges particularly poverty, illiteracy and malnutrition. In order to address these social and economic challenges, governments are keen to formulate appropriate short term and long term plans, in which the focus is to reduce poverty level, generate employment and to develop health and education services through public investment. The government earmarks the highest allocation for implementing education and health sector programs in almost every year in its annual development plan (ADP) in order to develop human resources.
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1.2.3 Poverty Trend and Poverty Reduction Initiatives Poverty reduction has been a major thrust in all planing documents in Bangladesh. In an attempt to alleviate poverty, „pro-poor‟ strategies have been implemented by successive governments over the years. In the early 1980s, national and international NGOs involved in poverty reduction activities particularly by providing micro credit as well as livelihood training to the poor. As the size of the poor population was very large to be handled by the government alone, NGOs and the private sector were encouraged involving in poverty reduction activities. As a result, poverty has reduced at a faster rate particularly from 2000 onward (BBS 2007a).
According to the Household Income and Expenditure Survey (HIES) 2005, the national head count rate3 of poverty measured by the upper poverty line declined from 59 percent in 1991/92 to 49 percent in 2000 and declined further to 40 percent in 2005 (BBS 2007a). This translates into an annual average rate of decline 1.9 percent during the period 1991/92 to 2000 and 3.6 percent during the period 2000 to 2005 (BBS 2007a). The higher reduction in urban poverty is explained by the increase in urban wages, increase in remittances sent by Bangladeshi live in abroad and a relatively large decline in family size. However, despite improvement in poverty reduction, the income share of the poorest population quintile declined from 8.3 percent in 1991/92 to 5.6 percent in 2005 (BBS 2007a) indicating extreme poor have not benefited significantly from the growth process. Rural poverty has reduced relatively less than urban poverty. Female headed households (FHHs) are found to be more poverty prone than male headed households (MHHs). Moreover, income inequality has increased from 0.39 in 1991 to 0.47 in 2005 at an average annual rate of 1.47 percent (BBS 2007a), which lessen the growth of benefits. Therefore, reducing poverty to a tolerable level remains a central development challenge in Bangladesh (GoB 2005b). It is important to note that poverty related statistics are taken from Household Income and Expenditure Survey (HIES), 1991/92, 1995/96, 2000
3 All poverty related statistics are taken from Household Income and Expenditure Survey (HIES), 1991/92, 1995/96, 2000 and 2005 conducted by Bangladesh Bureau of Statistics (BBS), Ministry of Planning. The Government of Bangladesh.
7 and 2005 conducted by Bangladesh Bureau of Statistics (BBS), Ministry of Planning, the Government of Bangladesh. In order to reduce poverty, the focus of the „National Strategy for Accelerated Poverty Reduction (NSAPR I and II) is to create an enabling environment combined with appropriate policies, strategies and programs. The identified critical sectors contributing in poverty reduction are: agriculture and forestry; human resource development particularly education and health; power and energy; transport and communication; food security and social safety net programs (SSNPs); small and medium-term enterprises (SME); and micro-credit (GoB 2005b). The strategies also aimed to tackle emerging issues such as food security and climate change. Climate change and its variability have already impacted on life and livelihoods of the people particularly in the coastal areas of Bangladesh (GoB 2005a). A significant proportion of the population could be displaced in Bangladesh through climate induced flooding, tropical cyclones and storm surges. In the context of climate change, food security is becoming an emerging issue in
Bangladesh which is also highly related with nutrition particularly child nutrition.
1.2.4 Nutritional Status and Policies The food security situation in Bangladesh has improved in terms of availability but almost half of the population is still far from being food secure. Nearly half of the children in Bangladesh are either moderately stunted or underweight which makes it one of the most severe cases of malnutrition in the world (World Bank 2005). The average Bangladeshi diet is deficit in energy by about 15 percent. It is seriously unbalanced with an inadequate intake of fat, oil, fish/animal protein, fruit and vegetables (BBS 2007a; HKI/Bangladesh 1998). Most poor people in Bangladesh suffer from either chronic or transitory food insecurity due to lack of ability to buy food or sudden and unpredictable shocks such as flood, drought. Increase of food price in lean period of harvesting, calorie consumption falls noticeably particularly in the rural areas. The seasonal fluctuations in calorie consumption impact on child nutrition enormously. Although, the average calorie intake level has improved during the 1990s in Bangladesh, the gain in nutritional intake is not so impressive and thus, large scale malnutrition persists over the country. Eighty per cent of the dietary energy supply of Bangladeshis still comes from cereals (HKI/Bangladesh 1998). High consumption of
8 cereals with low intake of pulses and animal based protein results in high levels of anaemia and other micro nutrient deficiencies. The BDHS 2007 report showed that, the country made significant progress in reducing malnutrition in the 1990s, but this progress has slowed down since 2000 to till date. Child underweight reduced two percentage points only from 48 percent in 2000 to 46 percent in 2007 (NIPORT 2008). The government of Bangladesh has recognised nutrition as one of the major issues to ensure public health, particularly child health. The national nutrition program (NNP) is being implemented for the period of 2003-2011 aiming to improve the nutritional status of the vulnerable groups, especially women and children through providing nutritional supplement and behavioural change communication (GoB 2008b). However, the program was extended up to the period of 2011-2016.
1.2.5 Educational Attainment and Policies The government of Bangladesh nationalised the primary education sector in 1973. In Bangladesh, primary education was declared as „Universal Primary Education‟ with special attention to girls‟ education in 1990. Along with the enactment of the Compulsory Primary Education Act in 1990, various programmes like free supply of text books up to secondary level ( class I-X) for all children, tuition fee waiver for all children in government owned primary schools, cash incentives provided to children from poor families, free education for girls‟ up to class VIII4, special programs for increasing social motivation and physical facilities in schools, recruitment of qualified teachers, particularly female teachers in primary schools contributed to higher primary school enrolment for both boys and girls. As a result, the enrolment rate at primary school increased from 60 percent in 1990 to 91 percent in 2008 for all children. Completion rates at primary school also increased from 40 percent in 1990 to 50 percent in 2008 (BANBEIS 2009). The strategic goal for gender equality was designed in consistent with MDG goal with regard to promoting gender equality and empowering women. A multi-sectoral approach was adopted in the areas of education, health and labour force participation. At secondary education, government has undertaken programs like free education for girls‟
4 Primary education (grade I-V) provided by the government school is free for both boys and girls while girls are offered free education up to class VIII and recently, it is extended up to class XII.
9 extended from class VIII to class XII including financial incentives. The underlying objective to provide these incentives is to increase average age of marriage to reduce population growth. Girls‟ education in particular, is recognised as a major thrust of human resource development and thus, the education sector receives the highest allocation in annul budget (GoB 2007a). The significant increase in enrolment and higher completion rate at primary school contribute to higher enrolment for all children at secondary school but importantly girls‟ enrolment is proportionately higher than the boys‟ enrolment. As a result of the expansion of education, women's participation in the labour market has been increased significantly in Bangladesh. The share of women in wage employment in the non- agricultural sector has increased over the years. Participation of women as professional and technical workers has also been improved and the participation rate is 25 per cent against their male counterparts, according to the Labour Force Survey 2005-06 (BBS 2007d). Despite these achievements, the education sector faces challenges in achieving higher literacy rate particularly for women as well as their (girls‟) low participation at tertiary education.
1.3 Research Problem
Although major socio-economic development indicators show considerable improvements in Bangladesh, more than one third of its citizens live below the poverty line (BBS 2007a). In the case of education, Bangladesh appears to be gaining a stable ground and the literacy rate for 15 years and above is on average 50 percent (BBS 2006a). The high level of illiteracy indicates that the country possesses a low level of knowledge and skills from the human capital point of view. In addition, around half of the children in Bangladesh suffer either from moderate stunting or underweight and thus the nutritional status of children has not improved since 2000 (NIPORT 2008). If a child is undernourished during his/her first two years of life - he/she is less likely to complete school and will earn, on average, a 10-17 percent lower income than a well nourished child (World Bank 2009). Therefore, it is an urgent need to build a strong, healthy and well educated workforce to support the economic development of a country like Bangladesh.
10 The poor nutritional status of female children is compounded by a lack of access to various services, resources and opportunities in later life. This, in turn, results in poor health and low birth weight babies who tend to be more malnourished in childhood and beyond. This is manifested in maternal malnutrition as 45 percent of Bangladeshi mothers are malnourished (body-mass index is less than the critical value of 18.5) as well as around 40 percent of children are born with low birth weight (below 2500gm) (GoB 2005b). Successive governments in Bangladesh have implemented various policies and programs to overcome the nutritional problem and some of these programmes/policies were successful as discussed in Chapters 3 and 4. Nevertheless, there is a need for rigorous investigation in human resource development to support policy formulation in this area. In Bangladesh, malnutrition is also the result of ignorance of people, particularly mother‟s regarding the need for a balanced diet comprising appropriate amounts of carbohydrate, protein, fat, vitamins, vegetables and other micro-nutrients. The food value reduced substantially by the cooking process, habit and nature of diet (timeliness, appropriate amount of food), feeding practice and lack of knowledge about health, nutrition and hygienic practices at home (ADB 2004). Many indigenous food items (for example, coarse rice, leafy vegetables, seasonal fruits like guava, pineapple) with rich food values are often ignored due to lack of knowledge and understanding. This is a concern when children from wealthier families suffer from malnutrition. The report of „MDGs: Needs Assessment and Costing (2009-2015)‟ showed that one third of malnourished children in Bangladesh were from the wealthiest quintile, which indicated that mother‟s education, food habits and hygienic practices are important factors in improving child nutrition (GoB 2009b). The country, therefore, cannot relax its fight against malnutrition because more than 60 percent of children (below six years of age) were malnourished in the poorest twenty percent of households (GoB 2009b). Bangladesh is, however, trapped into a cycle of low levels of child nutrition as well as low levels of education which together can cause an enormous wastage of potential productivity of the country‟s future workforce.
11 1.3.1 Need for Research in Women’s Education In 2008, the proportion of the illiterate population in Bangladesh was around fifty percent, indicating a very poor outcome of past investment in education. It also preformed poorly in its nutritional status particularly for children. Although, girls‟ enrolment at primary and secondary school increased, women‟s overall educational attainment is low. From the human capital perspective, the country is likely to be constrained severely in its development efforts by low productivity of the future workforce. A rigorous investigation is needed to identify the most beneficial outcomes which could be expected from the investment in women‟s education. Many policy makers, researchers and planners focus on the traditional approach of labour market relation with women‟s education ignoring their significant indirect contributions made at the household level. This study intends to provide a rigorous examination of indirect benefits of women‟s education by using household survey data from Bangladesh. Therefore, the research questions developed are as follows:
i) Whether mother‟s education significantly enhances children‟s school attendance? ii) Whether mother‟s education significantly improves their children‟s nutritional status?
1.4 Objectives of the Study Empirically, the study endeavours to investigate the influences of women‟s education on children‟s educational attainment and nutritional improvement. The investigation is to be performed in the existing socioeconomic setting of Bangladesh. Basic promise of this research is to suggest policy guidelines for protecting and nurturing potential productivity through enhancing women‟s education. Because a large number of children in Bangladesh receive relatively low levels of education and also suffer from malnutrition which cause a huge amount of wastage in human resources. The investigation is undertaken to address a number of objectives which are mentioned as follows.
12 Contribute to an understanding of the women‟s role being played in the economic development process in the low-income countries5 through their roles at the household level;
Look at the impact of mother‟s education in achieving improved educational attainment and nutritional outcomes for children;
Provide a detailed analysis of the inter-relationships between female education and children‟s educational attainment as well as nutritional improvement; and
Use the results of the study to develop policy recommendations applicable to Bangladesh and other similar developing countries to support investment in women‟s education, as a complement to other human capital policies and programmes already in place.
1.5 Theoretical Basis Human capital theory asserts that education promotes economic growth through providing education and nurturing labour force to support more productive economic activities (W. Schultz 1961, 1963; Becker 1962). Becker (1962) focuses on the impact of higher real wages in increasing the value of time and therefore the opportunity cost of home production such as child rearing. If investment in women‟s education is increased and they enter into the labour market, the opportunity cost of child rearing will rise. The increased earnings raise the desire to provide children with formal and costly education (Kingdon 1997; Keeley 2007). In the process of human capital accumulation, maintaining a sound nutritional base for children and the efforts to ensure their education has great significance. Education Watch 2002 shows that a higher proportion of women used their skills for personal communication and helping their children in school work than men do (CAMPE 2003). Mothers, who attended school, usually enrol all of their children and keep them in school longer (Asian Development Bank 2004). Therefore, improved nutrition and higher education for children due to their mother‟s education will have far reaching effects on children‟s future productivity. This justifies the investment in girls‟
5 The HDI statistics released by the United Nations Development Programme is based on health, education and income statistics. It classifies the countries into three levels: „low human development‟ indicates the country which scores HDI between 0.0 and 0.5, „medium human development‟ for scores between 0.5 and 0.8 and high human development for scores between 0.8 and 1.0.
13 education to promote economic development as a rational choice of societies (Kuruda 2009). Countries with low per capita income are also suggested to invest more on education, particularly girls‟ education, due to higher rate of returns to education (Psacharopoulos 1994; Haddad et al. 2003). This proposition is also supported from the economic efficiency point of view (Kingdon 1998, 2002).
In the debate of economic (direct) and social (indirect) benefits of women‟s education, the literature focused on social outcomes such as fertility reduction, improvement of children‟s health and nutrition, acquisition of higher education and other human capital attributes. In the case of social benefits, studies found positive associations with maternal education and in many cases, effects are significant compared to father‟s education (Barrera 1991; Sandiford et al. 1995; Mare and Maralani 2005, Boyle et al. 2006). On the other hand, in the case of economic benefits, studies mostly concentrated on labour market outcomes to estimate the women‟s productivity as a rate of returns to education (Psacharopoulos 1994; P. Schultz 2002; Aslam 2007; Asadullah 2009).
Studies also examined the relationship between women‟s education and economic growth (Hill & King 1993; Schultz 2002). In the context of developing countries, since women are not often regularly involved in the formal labour market, the value of their economic contributions has been debatable (Gannicott and Avalos 1994; Kuruda 2009). However, it is generally observed that education increases women‟s labour force participation and thereby enhances their productivity. More importantly, although the ratio of economic contribution of male to female is similar, the total contribution tends to lower for women compared to men (P. Schultz 2002; Aslam 2007). These controversies in labour market analysis are explained by labour market discrimination (Kingdon 2002) and lower level of education and cultural attitudes towards women (Cameron, Dowling & Worswick 2001; Cong 2008; Hosgor and Smits 2008). However, major features observed in the literature that women on average receive low level of education and due to average short tenure in the labour market, their contribution to the economy is considerably low.
In this context, an inevitable question arises, if educated women do not participate in the labour market, how much they contribute at the household level. In an econometric framework, this investigation is attempted to solve this question by using household level
14 data from Bangladesh. Two vital factors of human capital such as children‟s school attendance and their nutritional status are included in the econometric models in order to identify the influence of mother‟s education. This is justified by the assertion of Hill and King (1993) that education equips women with knowledge and skills to perform their roles at home more effectively. At home, women‟s education has a greater effect on family welfare than men‟s education because educated mother appeared to provide better hygiene, improved nutritional practices and greater effective efforts in caring for the family‟s health. It is also likely that mother‟s education is most influential to produce children‟s human capital by ensuring formal schooling (Sandiford et al. 1995). The findings in relation to the indirect benefits from women‟s education will be used to strengthen the arguments for increasing investment in girls‟ education in Bangladesh as well as to support similar arguments for other developing countries those face similar type of problems. 1.6 Methodology
The focus of this thesis is to examine two important relationships of mother‟s education with: i) children‟s school attendance, and ii) nutritional status from the perspective of human capital theory. Broadly, the methodology of this thesis has two parts. First, literature on women‟s education and its impact - both theory and empirical findings - in the framework of developing countries would be reviewed (shown in Chapter 2). The chapter needs to include an overview of human capital theory including its application to women‟s education. It reviews analyses of the economic and social benefits of women‟s education. Costs of educating children, relationships between women‟s education and children‟s educational attainment as well as children‟s nutritional status are also included. Factors associated with low level of women‟s education, its consequences and arguments relating to primary and post-primary education are also presented. The second component of review covers the situations of Bangladesh regarding education, health and nutrition, relevant policies, constraints and challenges in order to make the analysis consistent from the perspective of human capital theory. These are described in Chapters 3 and 4. A comparison of these situations among the South Asian countries is also done.
15 Second part of the methodology involved empirical investigation which is to be performed within an econometric framework using household level data from Bangladesh. Depending on the setting of socio-economic conditions of Bangladesh and available data, models are specified with relevant explanatory variables. In both models, focus is on parent‟s individual levels of education particularly mother‟s, although other model specific variables are also included. In the case of school attendance, probit model is useful (Equation 5.9) because „school attendance‟ is a qualitative dichotomous dependent variable which falls into a „yes‟ or „no‟ category. In this case, model is estimated by using differential probit (dprobit) command in Stata version 10. In school attendance function, two models are to estimate: one for primary school attendance and the other for secondary school attendance. In both cases, models are estimated from the national and the rural perspective. While in the case of child nutrition, multiple regression model (Equation 5.15) is appropriate due to the quantitative nature of the dependent variables. Child nutrition is estimated in terms of a child being stunted (low height for age) or underweight (low weight for age). Similar to the school attendance model, the national and the rural situation are also to be estimated. In estimating the child nutrition model OLS method will be used. Data regarding „Household Income and Expenditure Survey 2000‟ along with Community Survey and „Child Nutrition Survey 2000‟ from Bangladesh are used in this investigation. The econometric models and the respective hypotheses are developed in Chapter 5, based on the literature regarding women‟s education. These equations will be solved with the help of statistical tool Stata Version 10. However, the use of a quantitative approach is justified by the availability of household level data, which covers a wide range of variables related to the investigation.
1.7 Structure of the Thesis This thesis is organized as follows. Chapter 1 is the introductory part of this thesis. Chapter 2 presents a comprehensive review of literature on women‟s education, which forms the theoretical basis of this thesis. This chapter extends the discussion of human capital theory and its determinants in relation to women‟s education. It also describes the economic and social benefits and costs of women‟s education, the
16 consequences of lower levels of women‟s education and factors associated with lower levels of women‟s education. Moreover, this chapter explains the necessity of analysing both primary and post-primary education in the context of developing countries. Chapter 3 provides an overview of the education system in Bangladesh, policies and programs undertaken by successive governments and the constraints and challenges faced by the system. This chapter focuses on how much the country has achieved to date at various education levels in relation to women‟s education and how this contributes to increasing children‟s educational attainment, as low levels of education are still a reality in Bangladesh. This chapter also presents a comparative analysis on women‟s educational status, such as enrolment rates at primary and secondary levels, completion rate of primary school and compulsory years of schooling, with other South Asian countries, such as India, Pakistan, Nepal and Sri Lanka.
Chapter 4 provides an overview of the health system in Bangladesh. This chapter also focuses on the present situation of women‟s health and their contribution associated with improvements to children‟s health and nutrition, as these are serious concerns in Bangladesh. This chapter also presents a comparative analysis on women‟s health status, with other South Asian countries, such as India, Pakistan, Nepal and Sri Lanka.
Chapter 5 discusses the methodology used in this study. This chapter develops an aggregated framework outlining the methods used including models and hypotheses to investigate the effects assigned for the study are developed. Econometric models are specified in order to conduct the empirical estimation on the effects of maternal education on children‟s school attendance and nutritional status. The sources and limitation of the data used in this study are also discussed in this chapter.
Chapter 6 contains the empirical results based on the equation regarding children‟s school attendance developed in chapter 5. The explanation of dependent and independent variables and their expected relationships are also discussed in this chapter. The chapter analyses the estimated results in detail, particularly the income effect and the effect of parent‟s education.
Empirical results on child nutrition are presented in Chapter 7. Results are summarised based on the Equations 5.16 and 5.17 formulated in chapter 5. Major
17 findings on the effect of parent‟s education on child nutrition are also presented in this chapter. Chapter 8 concludes the study. The major empirical findings of the study and their policy implications are analysed in this chapter.
1.8 Definitions of Terms
Various terminologies and concepts used throughout this thesis need to be defined in accordance with their specific applications to the research questions (Detailed definition is given in Appendix D). These are described in the following.
School Attendance (primary level): Primary school attendance is defined as attending classes one to five (I-V) by the children aged 6-10 years. The official age group for primary school in Bangladesh is 6-10 years. Primary school attendance is used as dependent variable to estimate the effect of maternal education on children‟s educational attainment.
School Attendance (secondary level): Secondary school attendance corresponds to the classes VI to XII for children aged 11 to17 years. The official age for secondary school is 11 to 15 years. To capture the higher number of students at the secondary level, in this thesis, secondary school age is considered for 11 to17 years. Because most children in Bangladesh take 1 to 2 years more to complete their 5-year primary school cycle and therefore enter secondary school at an older age than officially recognised (CAMPE 2003). Similar to primary school attendance, secondary school attendance is also used as dependent variable to estimate the effect of maternal education on children‟s educational attainment.
Household (HH): Household consists of a group of persons living together and taking food from same kitchen. Household and dwelling household are used synonymously (BBS 2007a).
Labour Force/ Economically Active Population: Economically active population or labour force is defined as persons aged 15 to 65 years who are either employed or unemployed during the reference period of the survey (preceding week of the day of survey enumeration). It includes employers, own account workers/self employed persons/ commissioned agents, employees and salaried employers, wage earners, paid family
18 workers, members of producers‟ co-operative and the persons not classifiable by status (BBS 2004).
Female: The term „female‟ indicates a common meaning for gender irrespective of age. Women and female are used synonymously in this thesis.
Girl: The term “girl” is defined as those of female who belong to the age group 0-17 years. Throughout the thesis, the term „girl‟ is used when education investment is discussed.
Woman: Woman refers throughout the thesis to those of female who are aged 18 years and above.
Adult Literacy Rate (15 + years) : The adult literacy rate is the percentage of literate person aged 15 years and above divided by the total population aged 15 years and above and this is multiplied by one hundred.
Net Primary Enrolment Rate: Net enrolment rate refers to the number of pupils in the official school age group 6 to 10 years, in a given school year, expressed as percentage of the corresponding population of eligible official age group (GoB 2009b).
Gross Primary Enrolment Rate: It refers to the number of children enrolled at the primary level (grades I - V) regardless of age.
Dropout Rate: Percentage of students of a certain grade, who dropped out during a certain year as a proportion of the students registered in the same class during the year.
Completion Rate (primary level): The completion rate is the percentage of students completing 5-year cycle of primary education among the students enrolled in classes one to five (I-V).
Completion Rate (secondary level): The completion rate is the percentage of students completing 10 year cycle of secondary education among the students enrolled in classes six to twelve (VI-XII).
Stunting (HAZ): Stunting indicates reduced linear growth (height or length) compared to the expected growth in a child of the same age. Stunting is usually the end-result of chronic and inadequate nutrition, which may show future complications and finally
19 impair the working capacity. This also indicates a long term effect on child health (BBS 2002b).
Underweight (WAZ): Underweight indicates a deficit in body weight compared to the expected weight for the same age, which may result either from a failure in growth or loss of body weight due to infections. Underweight is usually treated as short term phenomenon in expressing malnutrition (BBS 2002b).
20
Chapter 2
The Benefits from Educating Women: A Literature Review
It is recognised that education generally acts as a catalyst between human resources and economic development for both men and women. However, women‟s education deserves extra attention because of its positive effects on the life of women themselves as well as on their children. The World Bank Country Study on Pakistan (1989) highlighted that women‟s education has multiple contributions to human resource development. Firstly, women are contributors for health, nutrition and education for their children. Secondly, it reduces population growth by changing attitudes and behaviour towards modern contraceptive use, and finally it contributes to the economy through higher labour force participation. Thus, it is argued that women‟s education expedites the development process of a country, while the process is negatively impacted if women remain substantially illiterate (Velkoff 1998). Higher investment in girls‟ education is, therefore, strongly recommended as a development priority for those countries seeking higher economic growth. The objective of this chapter is to review human capital theory and its determinants, the rate of returns to education and the association of women‟s education with human capital. It also analyses the economic and social benefits of women‟s education with regard to higher productivity, lower fertility, lower infant and child mortality, improved nutrition and acquiring children. This chapter focuses on human capital theory and the contributions of women education to the socio-economic and cultural context of the developing countries. The chapter contains nine sections. Section one explains human capital theory, rate of returns to education and determinants of human capital. Section two explains the
21 economic and social benefits of women‟s education while section three discusses costs of educating children. Section four describes the relation between women‟s education and children‟s educational attainment. Similar to section four, section five analyses the relation between women‟s education and children‟s nutritional improvement. Section six presents the factors associated with low levels of women‟s education while section seven describes the consequences of low levels of education. Section eight highlights the arguments for primary education and post-primary education. Finally section nine concludes the chapter.
Broadly, three categories of studies relating to women‟s education are found. These are described below.
I. Many studies examined the impact of women‟s education on their own life as well as on their children‟s welfare. These have shown that child well-being6 is strongly and positively associated with women‟s education. These effects mainly occur via the transfer of knowledge, changes in attitudes, beliefs, or self confidence of the literate or via the networks or other contacts (Caldwell 1979 1993; Cockrane 1979; Barrera 1990, 1991; Hill & King 1993; Sandiford et al. 1995; Ainsworth, Beegle & Nayamete 1996; Glewwe 1999; Breierova & Duflo 2002; Haddad et al. 2003; Hannum & Buchmann 2005; Boyle et al. 2006; Iversen & Palmer-Jones 2008).
II. The second category identifies that mother‟s education exerts a strong role on children‟s educational attainment by influencing largely the behaviour, attitude and learning intensity of children as they have longer interaction with mothers especially in the early years of age (Hill & King 1993; Sandiford et al. 1995; Ermisch & Francesconi 2001; Mare & Maralani 2005; Boyle et al. 2006).
III. The third category focuses on the economic contribution by measuring the rate of returns to women‟s education considering wage earnings from labour market participation (Becker 1962, 1981; W. Schultz 1963; Rima 1981; Psacharopoulos 1994, 1996; Rosenzweig 1995; Alderman et al. 1996; Jolliffe 1998; Kingdon
6 Longevity, child morbidity and mortality, infant mortality and other health indicators.
22 1997, 2002; P. Schultz 1993, 2002, 2004; Aslam 2007; Asadullah, 2006, 2009). This category becomes more significant as individual‟s productivity is closely related with education and thus she can contribute directly to the economic growth. Labour force participation generally responds positively with women‟s education, although a non-linear relationship between these two has often been observed, in many developing countries. This is due to their irregular presence in the labour market and consequently, it becomes difficult to construct a model for estimating the expected rate of returns to women‟s education. However, women‟s education is positively associated with wage earnings and thus the rate of returns increases as the level of education increases.
These themes are developed in more detail in the following sections focusing on their application to developing countries.
2.1 Human Capital Theory
The particular economic theory underpinning resource allocation to education is commonly known as human capital theory. It explains the relationship between education and earnings of a person by estimating the expected rate of returns from education (W. Schultz 1961). According to Psacharopoulos (1996), education, beyond its many cognitive and cultural effects, also creates „capital‟ that is embodied in the person who receives it. However, students incur expenses during the period of study, which compensate later in life as the graduate earns more than people of the same age who have a lower level of education. Thus, the basic proposition of human capital theory is that costs of one‟s education are recouped later in life with a profit. According to the Organisation for Economic Co-operation and Development (OECD) definition, human capital is explained as the knowledge, skills, competencies and attributes embodied in individual that facilitate the creation of personal, social and economic well-being (Keeley 2007). Human capital, therefore, indicates the stock of accumulated knowledge and skills which is gained by an individual through gaining education and experiences that make him/her more productive. Human capital theory was extensively developed by W. Schultz (1961, 1963) and Becker (1962, 1964) and established in the economic growth and development literature
23 in the early 1960s. The theory is accepted by the international development organisations as well as individual governments to justify their spending on education (Kuruda 2009). W. Schultz (1961, 1963) has established the idea that education is the function of formulating human capital, which is the basis for economic growth. He conceptualised a model incorporating the cost of education including foregone earnings during the schooling of an individual, the private benefit of education on individual income and the social benefits of education on economic growth. Based on this concept of human capital, Becker (1962) developed the rate of returns analysis in explaining educational impact. Controlling the effects of labour market irregularities, he found that labour force participation associated positively with the level of education and subsequently the rate of returns to education increased. Psacharopoulos (1994) worked extensively on the rate of returns to education, while P. Schultz (2002, 2004) concentrated on women‟s labour force participation and its returns in the context of developing countries. Human capital theorists, thus, have made the largest contribution by constructing a theoretical base to analyse the contribution of education to economic development.
Although the focus of human capital theory is on the productivity of the workforce from acquiring education, Libenstein (1957) also highlighted that nutrition is an important determinant on account of its substantial contribution to increasing an individual‟s productivity. Relative to poorly nourished workers, better nutrition is associated with higher productivity. Becker (1962) has mentioned the activities that influence future real income through allocating resources to people as an investment in human capital. The avenues for investment in human capital comprise schooling, training for various trades, medical care, vitamin consumption and also opportunities of acquiring information about the economic system. Thus, expenditures for consumption in early years of age significantly contribute to improving the physical and mental abilities of people and thereby raise their real income prospects (Becker 1962, p9). Findings of Finlay (2006) showed that individuals invest in education to raise future income, while they invest in health to raise the probability of achieving this higher income as their life expectancy extends. He has mentioned in a growing economy, an individual, who is risk averse, first invests in health to increase the probability of achieving the higher income in the second period. He concluded that health investment is vital for positive economic
24 development while policies directed towards education or health programs that promote education will have sustained positive outcomes for economic growth. In the process of developing knowledge and skills, formal schooling is considered as the principal route (Sandiford et al. 1995; P. Schultz 2002; Boyle et al. 2006) while years of schooling, primary and secondary school completion rate and literacy rate are the indicators for assessing the level of human capital within a country. Tests on competencies such as basic mathematics, English literature or other subjects are also used as the measures of human capital (Jolliffe 1998). Keeley (2007) and Leeuwen (2007) stated that „average years of schooling‟ is a popular proxy in measuring human capital, although these measures do not capture the qualitative aspects. However, although it is difficult to find a unique measure to estimate the human capital, the cost-benefit approach based on rate of returns is the most commonly used method in this respect.
2.1.1 The Rate of Return to Education
Assessment of the relationship between earnings and education is the cornerstone of human capital theory. The differences in average mean earnings of graduates at successive levels of education reflect the premium associated with education investment. These premiums can be combined with the costs of investment in different levels of education that leads to a cost-benefit analysis of investment in education. Estimates of profitability of such investments in human capital are known as the „rate of returns to investment in education‟ (Psacharopoulos 1994). There are two measures of returns to education: the private rate of return and the social rate of return. The rate of return is calculated by considering the costs of education investment (outlays and foregone income) and the forthcoming earnings of education (future income). Kuruda (2009) calculates the discount rate by solving an equation which sets the discount value of costs and benefits over a determined number of years at zero. Therefore, in order to measure an individual‟s productivity, the formula for the rate of returns to education counts the stream of expected net earnings on the basis of age-earnings and education to establish its present value at an appropriate discount rate (Becker 1962). Mincer‟s (1974) approach of estimating individual earnings with additional years of schooling is a widely used measure in deriving private economic returns to education (Psacharopoulos & Patrinos 2004). Similarly, in the calculation of social rate of returns, the formula includes social
25 costs (public spending for education) and social benefits (additional income-tax paid by the individual to the government due to extra income). Although the rate of returns to education is a widely accepted measure for labour productivity, there remain some controversies in its empirical application. This is because people may misjudge the rate of return of a prospective education investment. For example, technical and vocational education may be more productive than general education for a particular society, while the rate of return calculation can yield a contrary result. Moreover, with a view to the greatly limited resources of low income families, they may have higher discount rates than those of higher income families. Further, the relevant income stream which is expected to be forthcoming after the completion of education and the direct and the indirect costs incurred while studying need to be computed appropriately. Economic analysis, therefore, needs to be cautious when rate of return is used as a diagnostic tool in searching the alternative education policies for investment priorities (Psacharopoulos 1996; Kingdon 1998; Aslam 2007; Asadullah 2009). In exploring the cost effectiveness of alternative education investments in low- income countries, Glewwe and Jacoby (1994) found in their study „Students Achievement and Schooling Choice in Low-income Countries‟ that repairing classrooms is a cost- effective investment in Ghana relative to providing more instructional materials and improving teacher‟s quality. While in Pakistan and in Bangladesh, separate schools for girls, boundary walls and toilet facilities are considered as crucial factors for improving girls‟ enrolment particularly at the secondary level education (Bellew, Raney & Subbarao 1992). In a study, Asadullah (2009) noticed no wage advantage of private school graduates in Bangladesh while Pakistani private school graduates were found to earn more than their public school counterparts. The result suggests the relative superiority of private schools in Pakistan in terms of wage premium arising from education in these schools. The contrast between the two countries (Pakistan and Bangladesh) was explained as the divergence in public policy towards private schools that faced regulatory systems (Asadullah 2009).
26 Shafiq (2007) found that the rate of return to education (RORE) is traditionally used by the economists to understand the household decision regarding education. A negative RORE implies that education is an unattractive investment for the household. Accordingly, it may decide not to enrol its children in school. Conversely, a positive RORE encourages household‟s investment in education. A positive RORE, which exceeds the return, is required for the investment to occur. Rosenzweig (1995) has investigated the proposition that schooling improves productivity based on the notion that this process enhances information. Also, positive returns to schooling exist where productive learning opportunities can be exploited. The empirical findings suggest that schooling has high returns and the returns to learning are also high. The work of Rosenzweig and Foster (1996) in India found that school enrolment rates in India rose more rapidly in the areas which experienced technical change compared with stagnant areas. Indian farmers believed that investment in schooling has high values where the challenges of mastering new technologies are continuing. Thus, in an environment where learning has pay offs due to the introduction of new technologies, schooling can have large values and investments in education respond positively to those opportunities. But in the absence of learning opportunities, education investments are no longer profitable. Jolliffe (1998) in his paper „Skills, Schooling and Household Income in Ghana‟ showed that the returns to human capital focused on total household income including farm and off-farm income rather than wage income only. The reason was that the large majority of households in developing countries had self-employed workers, not wage earners. Also in Ghana, during the period of study approximately seventy percent of households were engaged in farm works. The study detected a positive effect of education on farm production, although the statistical significance was weak. Jamison and Lau (1982) found no support for the hypothesis that there were returns to education for African farmers in their studies that measured returns to the education of farmers. The lack of a significant effect of schooling on farm profit was attributed either to the low technological level of production or the absence of technological change in Africa (Jolliffe 1998).
27 Becker (1993), however, stressed the importance of technical knowledge and stated that the expansion of scientific and technical knowledge had raised the productivity of labour force and other inputs in production. The systematic application of scientific knowledge to production has greatly increased the value of education. This is because knowledge has become embodied in people (scientists, scholars, technicians, managers and other contributors to output). Keeley (2007) stated those countries that managed persistent growth in income had also large increase in the education and the training of their labour forces. Human capital theory basically assesses the relationship between rate of returns and the levels of education. Within the limited resources, it provides the indication how a profitable education investment could be made among the alternative ways. Rate of return shows the alternative choices whether private education is more profitable than public education etc. However, rate of return depends on quality of education, how much technological adaptation an education system can absorb. If an education system equipped with high technical knowledge, its rate of return will be higher than the traditional system. A rate of return analysis provides an indication of investing more in profitable education among the alternative options. Although, the rate of return technique has some limitations, still it is an important tool to assess the profitability of investment particularly in education. Nonetheless, the most important issue regarding education is its capability of adaptation of technical knowledge and innovation which raise the productivity of labour force.
2.1.2 Determinants of Human Capital
Human capital and education are generally considered synonymously. Studies on human capital are largely based on the relation between years of schooling and wage earnings from labour market participation (Kingdon 1998; P. Schultz 2002; Aslam 2007; Asadullah 2009). Becker (1962) defined the activities which improve an individual‟s productivity as investment on human capital, including nutrition, vitamin consumption, schooling and on-the-job training. Many studies have stressed the characteristics of the family where children are born and brought up and treated as the important components for human capital formation (Khandaker 1996; Ermicsh & Francesconi 2001; Mare & Maralani 2005).
28 Becker (1993), however, mentioned that families, particularly parents have a large influence on their children‟s acquisition of knowledge, skills, values and other human attributes. Children learn more easily when they are better prepared by their families and this learning process has multiplier effects in their future life. If a family belongs to the higher income level with educated parents, children‟s educational attainments are expected to be higher. Ermisch and Francesconi (2001) found that parent‟s education is a very powerful predictor of their children‟s educational attainments. The degree of association depends on parental resources, differential efficiency in the formation of children‟s human capital and bargaining power within the household. Parent‟s education has a significant positive effect on the probability of school enrolment among both boys and girls but the effect of maternal education is much more important than father‟s education in determining girls‟ enrolment decisions (Dostie & Jayaraman 2006). Parsons (1975) has identified the family as a principal social institution in the market economy that fosters income inequality through behaviour that forges inter-generational link between parents‟ and children‟s wealth. Khandaker (1996) has also emphasized household factors and mentioned, in most societies, the demand for children‟s education is centred on family decisions. Behrmen and Knowels (1999) examined the relationship between schooling indicators and household income in Vietnam. They found all four indicators - i) age when started school, ii) grade passed per year of schooling, iii) last completed grade, and iv) examination score in last completed grade of schooling - were significantly associated with higher levels of income. On average, children from higher income groups started school a quarter of year (0.25) earlier, completed two more grades and scored 17 per cent higher in the last completed grade than children from the lower income groups. They also found that girls‟ schooling responded more predominantly compared to boys at higher levels of household income. Similarly, Stromquist (1988) showed that the family‟s economic condition and expectation about the role of women were the crucial factors in explaining educational achievement in developing countries. Children from the families in the bottom quintile were found to attain lower education. By contrast, children where parents were homeowners had much higher educational attainment (Ermisch & Francesconi 2001).
29 Dostie and Jayaraman (2006) have shown in India that school attainment increases
income and expected returns to education given the school infrastructure. Using data from Ghana, Glewwe and Jacoby (1994) have observed that children with higher imputed ability start school earlier, stay in school longer and perform better both in Mathematics and reading tests provided that parents make appropriate schooling decisions for their children including when to enrol, how often to attend and which school to attend. They have also found that mother‟s education has a significant positive impact on a child‟s test scores both in Mathematics and in English, while father‟s education generated virtually no effects. Further, children in Ghana were attracted to better quality middle grade schools while quality of school has no significant influence on educational achievements. Heckman and Vytlacil (2001) emphasized student‟s ability and stated that increase in the returns to education could be attributed to an increase in ability. Becker (1975) indicated that although investment in education and labour force participation were two different issues, in most cases, the expected returns from labour force participation influence education decisions (Kingdon 2002; Dostie & Jayaraman 2006). A decision to invest resources in children‟s education is taken on the presumption that the returns from future labour market participation will be higher. In addition, attitude, learning processes and academic skills acquired at the elementary school also contribute to determining subsequent investments in education, because future returns are expected to be higher for those children that are keen to study. While in the context of developing countries, since parents cannot afford to educate all of their offspring due to financial constraints (CAMPE 2003), they are motivated to educate only those children who exhibit comparatively higher ability. Consequently, weaker performing children particularly girls are excluded (Bellew, Raney & Subbarao 1992). Further, the argument has been developed, since girls are more involved with household activities and consequently demonstrate poorer scholastic performances, there is likely to be less investment in girls‟ education compared to boys in the same household (Becker1962; Hadden & London1996; Kingdon 2002). Family is thus viewed by Parsons (1975) as an important unit with respect to making education investments for its members. Household characteristics such as income, parent‟s education, household size, time allocation and
30 attitude towards child, expected earnings and children‟s imputed ability are the major factors in determining current education investment to the children.
Human capital theory and its application have great significance which directs investment towards profitable one among the alternative investments. Investing on education and its quality to equip persons about modern technological innovation, now a day become the core of economic development. Similarly, by investing more on children‟s schooling and maintaining good nutritional base in the household, educated women can contribute to children to become potential work force. Therefore, the indirect way to invest on children by the educated women accumulates extensive human capital.
2. 1.3 Application of Human Capital Theory to Women’s Education
The role of women‟s education on human capital formation has been recognised as a priority in many developing countries in recent years. Educated women improve a country‟s stock of capital by acquiring knowledge and skills of value in themselves as well as enhancing their own productivity through increased labour force participation. Concurrently, they propagate future economic growth by rearing fewer, healthier and educated children (Bellew, Raney & Subbarao 1992). Thus, the highest importance was placed on girls‟ education and it is argued that considering all the benefits, investment in educating girls might well be the highest-return investment available in the developing world (Hill & King 1993; Summers 1994). The participation of educated women in the labour market, the ensuing incremental income growth and investment in human resources, especially education for girls, were also underscored by the World Bank (Bellew, Raney & Subbarao 1992). Gannicott and Avalos (1994) studied women‟s educational performances in several Melanesian countries namely Fiji, Papua New Guinea (PNG), Solomon Islands and Vanuatu. The average years of schooling in these countries except for Fiji were significantly below the average in the Asia Pacific Region and thus fell short of their aspirations for economic performance (as shown in Appendix TableA1.2). Fiji had the highest scores in terms of average years of schooling as well as in achieving gender parity both at primary and secondary education. It performed well in economic growth in the region. It is observed that increased women‟s education resulted in an extended
31 percentage of their participation in the workforce (Becker 1962; Cong 2008). The Labour Force of Survey 1997, Singapore (Ministry of Labour 1997) reported that in Singapore, the proportion of economically active women in the total labour force rose from 37 percent in 1986 to 42 percent in 1996, whilst the corresponding proportion of women with upper secondary education went from 53 to 66 percent. During the same period (between 1986 and 1996), employment of women increased by 50 percent, of which more than 87 percent had completed secondary schooling. This qualitative transition in the labour force accommodated with higher employment opportunities significantly contributed to economic growth of Singapore. This is supported by Cong (2008) in the study of women‟s labour force participation in the Asia Pacific countries. However, the linkage between women‟s education and economic development is shown by the following Diagram 2.1.
Diagram 2.1: Women Education and Economic Development
Economic Higher Individual Higher Labour Development Productivity/ Force Earnings Participation
Women’s Education
Higher Human „School Nutritional Capital Attending‟ by Improvement of Accumulation Children Children
Source: Author‟s articulation based on literature review regarding women‟s education.
Diagram 2.1 shows that education augments women‟s productivity through their higher participation in the labour market because educated women are more likely to be involved in the labour force. This amplified productivity contributes to higher economic growth. Increased education also transforms the labour force to a qualitative one, which
32 is more adaptable to modern technological changes. Thus, an expansion in women‟s education scales up the stock of human capital of a country. It also improves children‟s potential (future) productivity by enhancing knowledge and skills as well as improving nutrition at household level.
2. 2 Benefits of Women’s Education
The economic (direct) and the social (indirect) benefits of women‟s education are discussed in the following sections.
2.2.1 Economic Benefits The benefit that accrues from a person‟s (either man or women) increased productivity through enhanced labour market participation is commonly known as economic benefit. The economic benefits of women education are analysed in terms of economic growth, labour force participation and wage differentials.
I. Economic Growth New growth theories place education and human resource development at the centre of explanation for long-term economic growth. Although, both male and female‟s education had a strong positive effect on economic growth, the impact of female education was higher as against male education (Benavot 1989). In explaining the contribution of women‟s education, Hill and King (1993) explored the relationship between female education and per capita gross national product (GNP), emphasising a distinct dimension of women‟s education focusing on gender disparity. Using data from several countries on female secondary school enrolment rates and per capita GNP, they observed that an increase in female secondary enrolment had a positive effect on future GNP. But large disparities between male and female enrolments had a substantial negative effect on the same. It was also evident that a one-year increase in schooling raised farm output by up to 5 per cent, even after allowing the effect of other factors (Gannicott & Avalos 1994, p18). According to Kingdon (1997), many studies showed positive correlations between a country‟s education efforts and its economic status and importantly, this causality had been attributed to education. P. Schultz (2002) analysed the relationship between education investment and economic growth for men and women separately. He mentioned that the countries which
33 promoted education equally for both men and women had on average grown faster. East Asia and South East Asian countries are the most relevant example in this regard. Conversely, many South and West Asia, Middle Eastern, North African and sub-Saharan African countries lagged behind as they invested relatively less in women‟s education. However, investment in formal and non-formal education for girls, with its exceptionally high social and economic returns (Hill & King 1993; Summers 1994), has proved to be one of the best means of achieving sustainable development and economic growth. By contrast, this assertion becomes weaker when Tilak (1989) found that the rate of returns to women‟s education became significantly lower after adjusting their labour force participation.
II. Women’s Labour Force Participation and the Rate of Return to Education
The results of cross-country analysis on women‟s labour force participation using household level data from five Asian countries Indonesia, Korea, the Philippines, Sri Lanka and Thailand carried out by Cameron, Dowling & Worswick (2001) indicated that tertiary education had a large impact on women‟s participation in most countries. Primary and intermediate educations were found to have little impacts on labour force participation. This relationship was found to vary significantly by country and this variation was affected by the cultural phenomena. In the countries where gender roles were more traditionally defined, as with Korea and Sri Lanka, women‟s increased education levels were unlikely to improve their labour force participation. In the countries where gender roles were less rigidly defined, like Thailand and Philippines, there was likely to be a stronger relationship between women‟s education and labour force participation. This is supported by Cong (2008) in a study of women‟s labour market in Asia Pacific counties. Referring to statistics from the International Labour Organisation (ILO 2004), he indicated that although female participation in the labour market of Asia had increased in recent years, little or no change has occurred in East Asia particularly in South Korea and also in Japan (Cong 2008). P. Schultz (1993, 2002) investigated the relationship between women‟s labour force participation and the rate of returns to education in the context of developing countries. Adjusting various effects such as irregular participation, his findings reveal
34 that the increase in wage rates associated with an additional year of a worker‟s schooling is the same for women as it is for men. Holding the level of education constant, the estimates of the private wage returns to education tend to be higher for women than for men. P. Schultz (2002) has also mentioned that in most low income countries, women rarely work for a wage and thus labour force participation is low. He pointed out three major factors determining woman‟s low participation in the labour market. These are: i) woman‟s low market opportunity; ii) sources of non-earned income and iii) the wage opportunities of her husband or extended family. Further, variables of family composition along with fertility jointly determine woman‟s allocation of time over her lifetime and thus, it is difficult to predict whether she participates in the labour market regularly or not. Consequently, although the rate of returns to education is in many cases higher for women than men, due to lower participation in the labour market, the overall returns in terms of earned income are lower for women than men (Aslam 2007). Psacharopoulos (1985, 1994) worked extensively on the rate of returns to education and summarised a number of studies regarding rate of returns in many developing countries. The investigation underscored a consistent pattern of very large returns to primary education, followed by secondary and post-secondary education. It stated that the average rate of returns to primary education was 29 per cent, the return to secondary education stood at 18 per cent while it numbered at 20 per cent for post- secondary education (Psacharopoulos 1994). This indicates that the highest returns to education occur at primary level and the returns tend to decline at secondary and tertiary education. This pattern of diminishing returns to education justifies the expansion basic education (primary) at first, before making large investments in more costly tertiary education particularly in low-income countries. Thus, logically, since girls tend to be concentrated at lower levels of education and also the returns are tended to be higher at those levels, higher investment in women for primary education could provide the higher returns (Psacharopoulos 1994).
Marshallsay (2008) examined women‟s education and labour force participation within the socio-economic and educational context of Lao People‟s Democratic Republic (PDR). She diagnosed that women played an important role in the economy of Lao PDR, although they were constrained in educational participation and thus in the labour market.
35 She mentioned that women‟s empowerment could be promoted by accessing productive resources and deriving skills through education and training, which ultimately contribute to the economic development.
Khandaker (1987) recognised a positive relationship between women‟s education and labour force participation in Bangladesh. He also found that the husband's education reduces his wife's labour force participation because of the positive wealth effects of potential earnings. It was observed in the Philippines that a secondary school educated male was nine percent less likely to have a working wife than a male with no education (Cameron, Dowling & Worswick 2001). The important indication found by Cameron et al. (2001) is that labour force participation rates were found to be higher for illiterate women and lower for women with primary or secondary education while again, graduates had higher participation rates. At lower levels of education, women‟s higher participation is explained by the pressing demand to earn in order to survive. Evidence from Bangladesh showed that labour force participation was higher for women with low education as manifested in garments‟ workers, of whom more than 90 percent were women while again the participation rate was higher for graduates and post-graduates women. P. Schultz (1993, 2002) investigated the relationship between women‟s labour force participation and the rate of returns to education in the context of developing countries. Adjusting various effects such as irregular participation, his findings reveal that the increase in wage rates associated with an additional year of a worker‟s schooling is the same for women as it is for men. Holding as the level of education constant, the estimates of the private wage returns to education tend to be higher for women than for men. P. Schultz (2002) has also mentioned that in most low income countries, women rarely work for a wage and thus labour force participation is low. He pointed out three major factors determining woman‟s low participation in the labour market. These are: i) the woman‟s low market opportunity; ii) sources of non-earned income and iii) the wage opportunities of her husband or extended family. Further, variables of family composition along with fertility jointly determine woman‟s allocation of time over her lifetime and thus it is difficult to predict whether she participates in the labour force regularly or not. Consequently, although the rate of returns to education is in many cases higher for
36 women than men, due to lower participation in the labour market, the overall returns in terms of earned income are lower for women than men (Aslam 2007). Psacharopoulos (1992 1994) worked extensively on the rate of returns to education and summarised a number of studies regarding rate of returns in many developing countries. The investigation underscored a consistent pattern of very large returns to primary education, followed by secondary and post-secondary education. It stated that the average rate of returns to primary education was 29 percent, the return to secondary education stood at 18 percent while it numbered at 20 percent for post- secondary education (Psacharopoulos 1994). This indicates that the highest returns to education occur at primary level and the returns tend to decline at secondary and tertiary education. This pattern of diminishing returns to education justifies the expansion basic education (primary) at first in low-income countries, before making large investments in more costly tertiary education. Thus, logically, since girls tend to be concentrated at lower levels of education and also the returns are inclined to be higher at those levels, higher investment in women for primary education could provide the higher returns (Psacharopoulos 1994, p211).
III Wage Discrimination Alderman et al. (1996) stated that the gap between male and female total returns might be attributed to wage discrimination in labour market. In particular, a strong attachment to traditional gender roles limit female‟s access to higher productivity sectors. Therefore, not only is women‟s participation in the labour market lower but their wages are also lesser. Benavot (1989) found, in many developing countries, in addition to women‟s lower participation in formal economic activities, they usually received low wage compared to men. Aslam (2007) has examined whether labour market analysis could explain the lower education of girls compared to boys' in Pakistan. If the labour market rewards women less than men, scare resources may be allocated efficiently although inequitably in the household. The investigation estimated returns to education for males and females engaged in wage employment in Pakistan using household data. Applying four estimation procedures: OLS; Heckmen two step; 2SLS, and household fixed effect - the findings pointed to a sizeable gender asymmetry in returns. The estimated returns to additional
37 years of education ranged between 7-11 per cent for men and between 13-18 per cent for women which indicated a higher incentive to invest in female education than male. Although the rate of returns to education was considerably lower for men in Pakistan, the total earnings were dramatically higher for men than women (Aslam 2007). This puzzle of men and women‟s different productive characteristics was not explained by the labour market and rather the reason may be the low level of eduction by women. However, studies recognised unambiguously the social benefits of women‟s education.
2.2.2 Social Benefits
Social benefits are discussed in terms of fertility, child mortality and positive externality of literacy issues. I. Fertility and Women’s Education A strong association between women‟s education and the level of fertility is observed in virtually all societies (Jejeebhoy 1995; Ainsworth, Beegle & Nayamete 1996; Dreze & Murthi 2001; Kuruda 2009). According to Mare and Maralani (2005), the most prevalent relationship between women‟s education and fertility is negative and this arises from delays in marriage, improved labour market opportunities, increased use of contraceptives, and weakening of women‟s traditional role of child bearing. They also mentioned that strength and the form of this relationship had varied considerably across societies and countries. Some societies exhibited positive associations between women‟s education and their number of children whereas others showed a non-monotonic pattern (Mare & Maralani 2005). Jejeebhoy (1995) found that familial issues, especially the expected number of babies, spacing of babies and the use of contraceptives mostly depend on women, and thus they essentially need sufficient education to take these decisions in an appropriate manner. Emphasising education, she argued that education had a large effect on demographic behaviour, affecting child mortality, health, fertility, and the use of contraceptives. Educated women are observed to have fewer children than uneducated women, irrespective of region, culture, or the level of development. Women‟s education also causes fertility to decline by increasing their bargaining power, attaining greater
38 control over their destiny as well as improving husband-wife communication in the household (Jejeebhoy 1995).
Ainsworth, Beegle and Nayamete (1996) explained the relation between women‟s education and fertility and also the relation between women‟s education and contraceptive use in a study of fourteen Sub-Saharan African countries. They found that women‟s education resulted in lower fertility through four main channels. These were explained as wage effect, higher demand for child‟s schooling, lower child mortality and more effective use of contraception. They found that mother‟s education raised the „price‟ of a child by increasing the opportunity cost of women‟s time in rearing children as well as the wage that women would earn in the work force. Women with more education become concerned about giving birth to healthy children, which lowers child mortality. Further, educated women use contraceptive more effectively and thus reduce the number of unanticipated pregnancies. In these fourteen countries, the study showed that average schooling among women of reproductive age was very low, from less than two to six years. The study also revealed that women with primary education had a negative relation with fertility in half of countries while secondary schooling was associated with substantially lower fertility in all countries. The study also showed that female education had a positive association with contraceptive use in all countries included in the study (Ainsworth, Beegle & Nayamete 1996). Hill and King (1993) explored the association between women‟s education and fertility and found that fertility associated with a specific level of female education was much lower compared to women having no education. Dreze and Murthi (2001) showed in India that female literacy had a negative effect on fertility rate. The estimated coefficient was highly significant, robust and stable. They found that female education was expected to reduce family size through accessibility to modern contraceptives, social norms, decreased dependences on sons for social status and old-age security. In a cross- national study, P. Schultz (1993) found that fully half of the effect of female schooling in lowering fertility operated through its effect in curtailing child mortality. Female education should be prioritised above male education when population growth imposes high social costs in a country (P. Schultz 2002).
39 There remain counter arguments. Women education can indirectly raise fertility by improving maternal health, reducing pathological sterility, compressing the duration of breastfeeding and reaping contraceptive benefits (Ainsworth, Beegle & Nayamete 1996). Barrera (1991) investigated the relationship between mother‟s education and breastfeeding of a child. It was found that although the breastfeeding period of educated women was shorter compared to the uneducated ones, due to supplementary food and proper nursing and health care, educated women improve child health more effectively. According to Mare and Maralani (2005), transition in socioeconomic characteristics changes the educational attainments of mothers and fathers affecting their fertility and thus the educational attainments of their offspring. In other words, women‟s schooling changes their fertility and marriage behaviour, which alter the relative numbers of offspring born to them. However, most studies concluded that women‟s education, in particular, is a powerful device to reduce reproduction (Cochrane 1979; P. Schultz 1993). Human capital theorists also modelled the negative relationship between education and fertility. Becker and Lewis (1973) suggested that education changes parents‟ attitude from quantity to quality of children. Within their limited resources, they realise the need to balance the number of children with education investments for them (Kuruda 2009). Sandiford et al. (1995) have stressed that formal education is more effective than the various health interventions. Thus, it can be expected that education has a negative effect on fertility.
II. Child Mortality and Women’s Education
The literature on child mortality has long recognised the importance of women‟s autonomy as a link between mother‟s education and child survival (Cochrane 1979). Caldwell (1979) pioneered the argument that mother‟s education is essential for the child‟s well-being. He also mentioned that education itself changes women‟s values, beliefs, power or knowledge; which in turn leads to lower child mortality either through better domestic care, or more effective use of health services. It provides particular benefits such as improved child nutrition and schooling and reduced fertility. Based on observed data, Sandiford et al. (1995) found that those who invest in educating their daughters, value the survival of their offspring more than those who do not send their daughters to school. Glewwe (1999) also stressed mother‟s years of schooling which was
40 positively associated with improving child health and nutrition. Formal education is considered as the most effective means of producing the highest benefit in this regard (Sandiford et al. 1995). Caldwell (1979) argued that women‟s education had a net effect on child survival that could not be explained by the household economic characteristics alone. A mother plays the central role in household‟s domestic activities and commonly acts as the family‟s health worker. Her performance in these areas depends on her education level which equips her with general as well as specific knowledge and provides the means and confidence to seek new ideas. Barrera (1990) found that mothers having more schooling gave better protection especially to younger children against an unhealthy environment relative to mothers with little schooling. In Bangladesh, the infant mortality rate was 88 per thousand live births if mothers had no education, the rate stood 28 for mothers with secondary education and it was only 9 for mothers having higher secondary education (BBS 1998). This is supported by the study of Cochrane (1979), which identified literacy as the most important variable, in explaining life expectancy (child survival) and also recognised that literacy is more important than the number of doctors per person. In addition, due to better hygiene, more effective health care and better fertility management through women‟s education, families contained fewer children with relatively better health, which in turn reduces child mortality. It was found in Papua New Guinea (PNG), that infant mortality reduced with the increase in mother‟s level of education (Gannicott & Avalos 1994).
Muhuri (1995) examined the differences in child mortality due to mother‟s education and by the health interventions undertaken using data from Bangladesh during 1981 and 1982. The study considered the interaction between maternal schooling and health interventions at the Matlab (research area) level. Intervention was treated as a central variable to assess its effect on child mortality, with reference to the causes of death and the intensity of interventions. He showed that the death rate of children aged 1- 4 years decreased substantially with an increase in the intensity of health interventions, if the mother had no schooling. Surprisingly, the estimation obtained no significant result when mothers were educated. The highest child mortality was 36.6 per thousand in the area without health intervention while it was 16.7 per thousand in the intensive area
41 under the intervention. However, the finding of little impact of maternal education on child mortality does not discard the importance of education. Educated women in the area under interventions indicated the lower costs of public health programs in terms of information, education and communication activities as they acted as substitutes of health programs. Moreover, although the health interventions reduced child mortality, they did not curtailed the excess mortality of girls‟ and the mortality disadvantages associated with poor economic conditions as was expected. Therefore, higher formal education for mothers was a recommendation as it was critically important for the promotion of child health and disease prevention rather than undertaking expensive health interventions (Muhuri 1995).
Using data from Nicaragua of a large cohort of women who became literate exclusively by adult education, Sandiford et al. (1995) showed that the socio-economic profiles of newly literate women were similar to those women who remained illiterate, and significantly worse than those were educated by formal schooling. Their study also strongly supported the critical role of education in child health and survival, independently of other social and economic advantages. According to the results of this study, the rate of survival was significantly higher among the children of women in the adult education group compared with those of illiterate women. Within the adult education group, survival rates were better for the children who were born after the program rather than before. Controlling for household wealth, education of spouse and parents, access to health services, water supply and sanitation by logistic regression did not greatly attenuate the gap in mortality or prevalence of malnutrition between the two groups. The mortality effect of adult education was equivalent to about two years of formal schooling (Sandiford et al. 1995, p15). Therefore, it can be concluded that education enhances women‟s knowledge, decision making power and confidence in interacting with the outside world and thus increases the survival rates of children. Jejeebhoy (1995) mentioned that the proportion of dead children (controlled for marital duration and age) was most strongly influenced by women‟s education, even after controlling for household‟s economic status, husband‟s education and ethnicity. Of the overall effect, seven per cent decline in child mortality for every additional year of schooling, nearly four per cent or more than half persists when all other covariates are
42 controlled. Several multivariate studies providing evidence of the direct effect of women‟s education on child mortality show that about half of the overall effect of women‟s education can be attributed to the direct net effects of women‟s education, even after household‟s economic status is controlled (Jejeebhoy 1995). It was also argued that literacy sharing within household having at least one person whether man or woman had large beneficial effects to the society. Several studies (Gibson 2001; Basu, Narayan & Ravallion 2002; Iversen & Palmer-Jones 2008) also emphasised the positive externalities of literacy sharing which had increased the benefits of education by providing information to the illiterate members in the household. According to Basu, Narayan & Ravallion 2002, literacy sharing shows that one person‟s literacy has spill over benefits for others within the same household. In this regard, Gibson (2001) mentioned that literacy sharing had the potentiality for creating an intra- household externality. Basu, Narayan & Ravallion (2002) found strong external positive effects of education on individual earnings using household data from Bangladesh. Holding a range of personal attributes constant, an illiterate adult earns significantly more in the off-farm employment, when at least one literate member living in that family. They also showed these effects were stronger and more robust in the case of women. This analysis was revisited later by Iversen and Palmer-Jones (2008) using more recent household survey data from Bangladesh. They argued that Basu, Narayan & Ravallion (2002) had attributed a considerable wage premium for women in off-farm employment which raised the productivity of illiterates living in the household having at least one literate person. The relationship between high wage premium and receipts of literacy externalities by women might not be due to higher labour productivity but to better network or bargaining power of illiterate women married into literate households. Iversen and Palmer-Jones (2008) argued that, if women are more efficient recipients of literacy externalities, this raises the possibility that men are more efficient transmitters of externalities. If this is happened, this would contrast the long established evidence showing positive literacy externalities caused by women which is referred by Basu, Narayan & Ravallion (2002) considering illiterate women and off-farm activities. On the other hand, Iversen and Palmer-Jones (2008) tested illiterate males and non-farm activities in their studies as
43 more men were literate and non-farm works were more available than off-farm activities. They suggested to caution in making comments on such an issue ignoring the contrary results. The above discussion demonstrated that education equips women with knowledge and skills to perform their roles at home more effectively by leading to better hygiene, improved nutritional practices and higher ability in caring for the family‟s health. However, the substantial impact of mother‟s education on child welfare is unambiguously supported by the number of studies.
2.3 Costs of Children’s Education
Families incur substantial financial expenditure as well as an opportunity cost in educating children. Even when education is public and tuition free, school attendance entails various costs such as contributions to the school, purchase of books and other materials and miscellaneous charges (Gannicott & Avalos 1994, p13). On the other hand, opportunity cost is the foregone earnings while students stay at schools. These are
explained below in detail.
2.3.1 Educational Costs by Gender Due to a variety of reasons such as proper dresses or clothing, travel costs, higher financial costs are involved in educating girls than boys‟ for the same year of schooling. Alderman et al. (1996)‟s study in Pakistan showed that books and supplies had involved on average 20 percent added expenditure for girls than boys‟ at primary school and about 5 percent more for girls than boys‟ at the middle school level. The reason for greater costs might be due to smaller size of classes for girls, which could not capture the distributional economies of scale (Alderman et al. 1996). Thus girls‟ schooling is more sensitive than boys to changes in costs (Glick 2008). Increases in tuition and other fees are often blamed for reduced enrolment of girls (see for example Stromquist 1999). In Bangladesh, expenditure incurred for children‟s education is continuously increasing, which is a major concern for poor parents to educate their children as reported by Education Watch 2002 (CAMPE 2003). Indirect costs of schooling embody foregone earnings which are sacrificed while the children stay at school. Evidence demonstrates that in most developing countries,
44 girls spend more time than boys at household activities such as fetching water, cooking, cleaning and caring for younger children (Gannicott & Avalos 1994). It is also obvious that families depend more on girls‟ contribution compared to boys‟ (Glick & Sahn 2000). Thus, the opportunity costs of sending daughters to school are certainly higher for the parents (Herz et al. 1991; P. Schultz 1993; Aslam 2007; Lincove 2009).
Parents are usually reluctant to send their daughters to school without appropriate clothing. Also, they have greater concern about the safety of young daughters in travelling long distance to school (Gannicott & Avalos 1994). In Egypt, the location of a school within 1 kilometre of a community resulted in an enrolment rate of 94 percent for boys and 74 percent for girls, but when the distance was 2 kilometres, boys‟ enrolment fell only by four percentage points while girls‟ enrolment plummeted by 10 percentage point (Bellew, Raney & Subbarao 1992). The presence of primary and junior high schools in a community leads to increase enrolment, while distance to school is negatively associated with enrolment as found by Hill & King (1991). Girls‟ education is constrained more than boys by the distance to school (Glick 2008) and thus, public investment that increases the availability of schools locally is likely to benefit girls‟ educational attainment. Alderman et al. (1996), by using the average travel time to school, showed that boys had travelled much longer than girls. Therefore, girls‟ primary schools were suggested to be located within 1.5 kilometres of a community compared with 2.5 kilometres for boys‟ schools. At the secondary school level, parental concerns become stronger which increased the perceived „psychic costs‟7 of daughter‟s schooling and adversely influenced the family‟s decision to enrol them (Gannicott & Avalos 1994). Families in a traditional society find it difficult to bear the opportunity cost of girls‟ schooling as they are needed to care for siblings, household work and farm work (Hill & King 1993). Expenditure for educating girls perceived to be higher compared with boys‟ and Table 2.1 summarises the ensuing benefits and costs of educating girls.
7 „Psychic costs‟ include the costs those are involved with distant away location of school due to safety and security concern for the girls after puberty, particularly in the rural and remote areas.
45
Table 2.1: Benefits and Costs of Educating Girls
Economic Social Benefits Individual Student: Higher earnings, Higher education for women themselves greater occupational mobility. as well as for their children, better fertility Parents: Higher family control, improved child nutrition, lower income. infant and child mortality, longer life expectancy. National Increased labour productivity, Higher education for existing population higher Gross National Product (women as a part) as well as for next (GNP), higher GNP growth, generation, reduction in population higher taxes on earnings. growth, healthier population, better functioning of economic and political activities. Costs
Direct Parents: Uniforms, supplies of Forgone earnings (opportunity cost of books, note-books, stationeries, time while staying at school). tuition fees, transport cost, expenditure for tutors, donation and other charges. Indirect Forgone earnings (opportunity Foregone output of children staying at cost of time while staying at school, Psychic costs for girls. school). Source: Gannicott, K. G & Avalos, B 1994, Women's Education and Economic Development in Melanesia, Pacific 2010, National Center for Development Studies (NCDS), Canberra Australia. Hill, M. A & King, E. M (eds) 1993, „Women's education in developing countries, Barriers, Benefits and Policies’, Washington D.C.
[
Table 2.1 depicts that the benefits of women education are significant ranging from improved productivity at individual level to a healthier, better nourished and well educated population. Education, in its own way, expands knowledge and skills which in turn increases women‟s individual productivity through higher participation in the labour market. These earnings are accumulated as higher gross national product which contributes to the growth of the economy. Additional taxes generated from women‟s enhanced productivity stimulate economic growth. Similarly, social contributions in the form of higher education for women themselves as well as for their children, fertility reduction, improved child nutrition and lower child mortality are also strong. The benefits generated at the individual level in turn contribute at the national level in terms of small size of population, skilled and educated workforce employed in the economy.
46 On the contrary, expenditures of schooling range from tuition and examination fees to subscriptions for various school functions and private tutoring. Opportunity cost is also important as this incurred while children staying at school and can not be involved in any income generating activities. However, by taking into account the benefits from women‟s education would expected to be sizeably higher compared to the costs.
2. 4 Women’s Education and Children’s Educational Attainment
A number of studies show the positive impact of maternal education on children‟s education and the effects are in some cases higher compared to father‟s education (Glick & Sahn 2000; Mare & Maralani, 2005). Studies also indicate that mother‟s education exerts a greater effort on the schooling of daughters (Muhuri 1995), which has far reaching effects as indicated by human capital theory. The importance of mother‟s education explained below: A study of female students in the Cairo Public School System found that gains in self-confidence were associated with the education of the mother. The more formal schooling a mother had, the greater the standards and expectations she generated for her daughters compared to those of less educated mothers (Hill & King 1991). According to Sandiford et al. (1995), women acquired certain values or beliefs from their parents that were correlated with health and survival of their offspring and these cultural attributes affected a parent‟s decision whether or not to educate their daughters. They also indicated that families who invested in their daughter‟s education had augmented the survival of their offspring compared to those who did not send their daughters to school (Sandiford et al. 1995). Thus, the association of women‟s education with children education or health would be a reflection of the transformation of educational effects passing from one generation to another. Importantly, this process impacts more on girls‟ education in successive generations. Using household survey data from Bangladesh, Ravallion and Wodon (2000) showed that children of better educated parents were more likely to attend school. Ainsworth, Beegle and Nayamete (1996) found that the wage benefits of education induce women to seek more education for their children. Moreover, women with more education have higher aspirations for their own children‟s education and motivate them to devote more in education.
47 Ermisch and Francesconi (2001) explored the impact of family background variables on young people‟s educational attainment by using British Household Panel Data and its retrospective family history information. According to them, there is likely to be a positive correlation between parents and child‟s education because of genetics and possibly also because of „cultural transmission‟, that is, highly educated parents may provide a better environment (e.g. books around the house) for producing human capital in their children. They showed that if both father and mother were educated, the academic score of children would be high but relative to a parent with no qualification, mother‟s education had a stronger association with her child‟s educational attainment than the education of the father. This proposition also justifies that educated mothers are able to provide healthy food and care to their children and exchange views and ideas by virtue of better knowledge and enhanced ability to change their immediate environment (Barrera
1991). Mare and Maralani (2005) found that the effects of women‟s education on educational attainment of the next generation are positive. According to their research, intergenerational social mobility is connected with the effects of positions, statuses, and resources of the family in which people are born and raised. They also emphasised the importance of possible effects of parent‟s education on the education and well being of their children in a developing society because these effects shape patterns of educational opportunity within that society. The characteristics of family affecting the educational attainment of children were highlighted in their research. Thus, the role played at household level by the educated women can be treated as a most valuable investment in order to eliminate the vicious cycle, low productivity-low income-high poverty in developing countries.
2.5 Women’s Education and Child Nutrition
A strong relationship between mother‟s education and child nutrition is found by several studies (Sandiford et al. 1995; Haddad et al. 2003) which is mostly influenced by
fertility management, nutritional practice and access to the health services by the family. It was found in Brazil that income in the hands of mothers had a four times larger impact on child nutrition than the equivalent income in the hands of fathers (Hill & King
48 1993). This is supported by Caldwell (1979) that education itself leads to changes in woman‟s values, beliefs, power or knowledge, which in turn lowers child mortality either through better domestic child care or more effective use of health services. Sandiford et al. (1995) underscored the importance of women‟s education and stated that the effects of providing ten years of schooling to each woman would be much higher than any other investment (Sandiford et al. 1995, p5). King and Hill (1993) also justifies that more education of the mother appears to lead better hygiene, improved nutritional practices and greater effectiveness in caring for the family‟s health. Similarly, education enabled Nigerian mothers to exploit local public health services effectively. In the Philippines, mothers‟ education was found to have a larger protective effect on child health in the communities those were without water-sealed toilets and further from outpatient health care facilities than in the areas that had better provisions (Hill & King 1993). The World Bank‟s MDG Report (2005) showed that the rate of underweight children in Bangladesh had declined encouragingly with an increase in female education. Studies also showed strong association between parental education and children‟s human capital and reported a variety of inputs in favour of child health such as number and timeliness of parental visits for health care and the likelihood of obtaining immunizations (Strauss & Thomas 1995). Using data on children‟s height-for- age in Papua New Guinea, Gibson (2001) found durable effects of adult literacy on children‟s nutritional status. Breierovia and Duflo (2002) expressed different opinions regarding the studies which reported that parental education was more assertively associated with the outcomes such as child mortality and other measures of children‟s welfare. They argued that part of the correlation between paternal education and human capital might be due to the influence of unobserved background variables correlated with education. The difference between the effects of maternal and paternal education is likely to be biased upward due to omitted variable bias, where this bias has larger values for girls compared to boys, since girls‟ education is determined largely by the family background variables than boys‟ education. This proposition is supported by W. Schultz (1974). In addition, educated women are likely to marry educated men who usually have higher income and proper care about their children. Using large scale school construction
49 program data of Indonesia from 1973 to 1978, Brierovia and Duflo (2002) showed that the variation of time and region in school construction programs had generated instrumental variables for average education in the household and in effect, differences in education between husband and wife. They found female education is a powerful determinant of age at marriage than male education. While, in reducing child mortality, female and male education seemed equally important factors. They suggested that the OLS estimates for differential effects of women and men‟s education may be biased by the failure to take into account the incidence of associative matting (Brierovia & Duflo 2002). In addressing the omitted variable bias, using data from Nicaragua, Wolfe and Behrman (1987) found that once the mother‟s family fixed effect is removed, the association between mother‟s education and child health disappears. However, the limitation of fixed effect methods is that they remove a large part of the variation in data and thus exacerbates the measurement error problem, which tends to bias the coefficients downward. Thus, the hypothesis indicating the difference between the effects of maternal and paternal education may not be estimated appropriately by using OLS method. In this respect, Basu (1999) stated that the mechanism that links education to child well-being is important because knowledge itself, along with cognitive capabilities, augmented access to modern health facilities, increased willingness to use modern medicine, improved bargaining power in the household vis-à-vis doctors, health professionals‟ availability and personality of husband influence the child well-being. However, although generally studies have demonstrated that education equips women with knowledge and skills to perform their roles at home more effectively by leading to better hygiene, improved nutritional practices and higher ability in caring for the family‟s health, in empirical investigation it is necessary to be cautious in estimating the effects of mother‟s education on child well-being due to the importance of the combined effect of both parents decisions on these activities.
2.6 Factors Associated with Lower Women’s Education
Although, girls‟ school participation is increasing throughout the world, a number of studies show that women receive on average less education than men (Gannicott &
50 Avalos 1994; Micklos 1996; Alderman et al. 1996; Glick 2008; Hosgor & Smit 2008)., The causes of gender inequities in educational attainment are described below:
Haddad et al. (1990) suggested that family‟s economic condition was the most important component in explaining the gender gap. The ability to send children to school depends upon having a certain level of income, which is necessary for a family to educate their children (Sandiford et al. 1995). It was found that all children from higher income households performed better at school, although girls‟ education responded at a higher rate with the increase of household income. At a lower level of household income, girls suffer much than boys in educational attainment (Glick & Sahn, 2000). The expectation about the role played by women determines their lower educational participation. Findings of Gannicott and Avalos (1994) indicated that the average amount of education received by women is smaller than that of men due to low demand for women labour and also due to low household income available to provide education to women. Kingdon (2002) explained the gender gap in education as a result of labour market discrimination against women and the preferential treatment of sons by the parents. On the contrary, P. Schultz‟s (2002) analysis on women‟s labour force participation revealed that the argument for labour market discrimination in interpreting gender differentials is weaker. This is supported by Aslam (2007), who illustrated the absence of labour market discrimination between male and female schooling in Pakistan. Her estimates displayed that an additional year of education offered higher returns to women than men. But surprisingly, total returns to women‟s education were lower compared to men. This may be justified by the men‟s higher participation in private schools than women or may be due to the error in measuring the labour market experiences for women. In many countries, traditionally parents prefer to award their sons more education than their daughters. This parental inclination is explained by Gannicott and Avalos (1994) as a rational response to the constraints imposed by poverty and to the expected returns determined by labour market operation. When the expected returns from schooling of daughters are lower vis-a-vis the costs, female education becomes an unattractive investment to parents. The higher economic benefits from boys drive parents to invest scarce resources in sons before daughters (Stromquist 1999).
51 In a study in Pakistan, Aslam (2007) found the coexistence of high returns to education for women and gender disparity against them in the household investment of education. This may be explained by the returns from daughters‟ education accruing to parents is much lower than that from a sons‟ education because of longer labour force participation by men compared to women. Further, investment in educating women yields future benefits to their extended families after marriage but the burden of costs is often borne by the parents alone. The returns to daughter‟s education are reaped largely by her in-laws while opportunity costs and/or direct costs for educating girls are borne by the parents (Bellew, Raney & Subbarao 1992; P. Schultz, 2002). According to Aslam (2007), the Pakistan Integrated Household Survey (PIHS) 2002 showed that only 6 percent of adult daughters aged over 21 years resided in their parental homes. In other words, a majority of girls belonging to the same age group were married and lived with in-laws or husband. It implies that returns of their education are reaped by the in-laws or the husbands rather than by the parents (Bellew, Raney & Subbaro 1992). The opportunity costs of sending daughters to school are in many cases higher because girls usually do more housework than boys. Stromquist (1988) argued that parents have the tendency to rely more on daughters than on sons for domestic and family work. Families in a traditional society find it difficult to bear the opportunity cost of girls‟ schooling due to their involvement with the care for siblings, household work and farm work (Herz et al. 1991; Hill & King 1993; P. Schultz, 1993). Evidence also demonstrated the differences in the time spent by girls relative to boys on home activities (Bellew, Raney & Subbarao 1992). In same age groups, girls spend more than twice as much time as boys‟ on household and farm tasks (Gannicott & Avalos 1994). Micklos (1996) stated that the jobs carried out by girls in the fields and at home were considered of greater value than education. In some cultures, there exists deeply rooted attitude that education is more important for men than for women (Hannum & Buchmann 2005). A study in Pakistan revealed that 51 per cent of urban mothers believed in religious education equivalent to one year of formal schooling was enough for their daughters. Religions like Islam and Hindu provide a restrictive environment for girls schooling in South Asian countries, although no systematic results have found against religion in deterring girls‟ education (Hill & King 1993). Early marriage is one of the
52 main barriers to female education in many developing countries. In Bangladesh, 75 per cent of ever-married girls were wedded at the age of 17 years. In India the corresponding age is 19 years and in Pakistan it is 22 years. On the contrary, single men married between 3.5 and 7.2 years latter than single women did (Hill & King 1993). Families‟ antipathy to secondary education for girls is much greater than it is for primary level because of higher direct costs and conflicts with marriageable age. In Bangladesh, 91 per cent parents wanted their sons to go on to the lower secondary level while their number decreased to 61 per cent for girls (Khan 1993). This implies that girls who work substantially more than their brothers, are less likely to attend school and are burdened with over work, which in turn result in their poorer academic performance (Gannicott & Avalos 1994). The Household Income and Expenditure Survey, 2000 of Bangladesh (BBS 2002) delineated that absence of girls‟ from schooling was 6.19 per cent, almost double than that of boys (3.95%)
Kuruda (2009) showed that in developing countries particularly in South Asia, the education of sons is more valuable to the parents than the education of daughters. In explaining this conservative culture, he pointed out two causative values. First sons are obliged to support the parents all through their life and second daughters are supposed to leave the parents after marriage. P. Schultz (2002) showed that this cultural arrangement discourages the parents from investing in the schooling for their daughters compared with their sons. In low income countries, Gannicott and Avalos (1994) argue that parents perceive that the financial and psychic costs of educating their daughters are much higher than the benefits they can expect to receive. In most Asian countries such as China, Hong Kong, Singapore and Taiwan, women occupy a traditional role which includes family responsibilities, passive behaviour and providing support to husbands, brothers or fathers. Cong (2008) reported that this phenomenon has been developed based on deep-rooted culture of thousands of years. Budhwar et al. (2005) also showed that similar perceptions in India i.e. traditional status, gender stereotyping and the expected role of women encourage the differential treatment for women. Girls lose their share in the intra- household allocation of education because of less parental incentives to educate daughters. The possible explanations of parents‟ differential treatment of sons and daughters in education are encapsulated as: entrenched beliefs about the gender division
53 of labour; an asymmetry in parental incentives to educate girls and boys due to son preference; parents may value the return to a child education that benefits them separately while the returns to daughter‟s education can often reaped by her in-laws.
However, Bellew, Raney & Subbarao 1992 argued that given the economics of poverty and the traditions prevailing in the countries, from the parents‟ perspective, male education is understandably regarded as a better investment while female education has been treated as a luxury and thus ill afforded. Thus, to parents, investing in girl‟s education may seem less attractive than that in boys‟ and thereby economic potential of girls‟ education is constrained at the household level as also reported by Hadden & London (1996). Benson and Yukongdi (2005a) pointed out that in the developing countries, although women‟s participation in labour force has increased in recent years, the gap between sexes still exists. Women usually possess less human capital, low level of education and due to short tenure of labour force participation, they are likely to have reduced managerial skills or experience to compete equally at the work place. Education has also been denied to many women because families and society place more emphasis on their role at home. They are kept at home to help the family subsist day to day and thus, they can not compete with boys for wages when they grow up (Bellew, Raney & Subbarao 1992). Even, when women have a similar level of education to their male counterparts, child birth and family responsibilities make it impossible for them to gain equivalent work experience (Becker 1962; Benson & Yukonsdi 2005a). Regarding East Asian countries, Cong (2008) stated that female participation in the labour market had increased in recent years across the world including Asia. He mentioned about little or no change occurred in female participation rate, in East Asia, while the corresponding rate increased in South-east Asia and South Asia. However, the gap between sexes in labour force participation still exists. The factors influencing girls‟ education are inextricably tied with economic, social and cultural causes and thus it becomes difficult to differentiate the individual effects of the factors. Many empirical studies have attached importance to the role of women‟s education for socio-economic development of a country (Herz et al.1991; Hill & King 1993; Hannum & Buchmann 2005). Therefore, it is important to address the
54 conservatism of social attitudes and parental inertia towards female education as well as reduction of wage discrimination in the labour market in order to improve girls‟ incentives to acquire more education (P. Schultz 2002).
2.7 Consequences of Women’s Lower Level Education A country may face profound consequences if its women remain illiterate and systematically deprived (Hill & King 1993; Gannicott & Avalos 1994; Lincove 2009). According to Bellew, Raney & Subbarao (1992), these exact a development cost in terms of lost opportunities to decrease population growth, increase income and productivity and improve the quality of life. If girls continue to receive less education than boys‟ this disproportionate educational attainment will significantly impede their own as well as their children‟s future productivity. In an extensive investigation in rural Pakistan, Alderman et al. (1996) identified a large and significant gender gap in school enrolments and cognitive skills which in turn resulted in larger gaps in productivity and in the command over resources. Kingdon (2002) argued that since economic gains from women‟s education are generally at least as high as those from men‟s, their educational backwardness is inequitable as well as economically inefficient.
Hill and King (1993) observed that the levels of women‟s education as well as gender parity in educational attainment significantly affected economic growth. Using data on female secondary enrolment rates and pooled data on GNP of 152 countries, they found that high female secondary enrolment had a positive effect on GNP, while large male-female enrolment disparities generated a significant negative effect on it. According to their interpretation, countries, where the ratio of female enrolments are less than 75 percent of male enrolments, can expect approximately 25 percent lower levels of GNP than other countries which have a similar development status but minimum gender gap. The Pacific Island countries, in Papua New Guinea (PNG), Solomon Islands and Vanuatu, where women received on average less education than males, had a large male- female disparity in enrolments, particularly Papua New Guinea and Solomon Islands with 58 percent and 63 percent respectively during 1990. According to Hill and King (1993), these countries are paying substantial prices in terms of foregone GNP growth.
55 Hill and King (1993) also diagnosed that the gap between men and women had an independent effect on social indicators such as life expectancy, infant mortality and fertility. Inside Pakistan, the low status attached to women contributes to high population growth which drags down the growth in per capita income. Furthermore, women do not contribute as much as they can due to systematic deprivation occurring in several spheres of life (World Bank 1989). Hence, an average economic growth of eight percent, although it is impressive, is not translated into improved standard of living in Pakistan as population grows on average at a high rate of more than two per cent per year (UNDP 2010). Many socio-economic indicators for Pakistan are lagging behind in the areas of health and education within the South Asian region (as discussed further in chapter 3 and chapter 4). Econometric analysis suggested that after controlling per capita income, female secondary school enrolment was a highly significant determinant for reduced desired family size, while male enrolment ratio had no impact on the same (Kingdon 1997). Kingdon (1997) also showed that the benefits from expanding female education were far greater than the benefits from other public interventions such as improving family planning services or increasing the number of physicians. It is evident that in comparison with various investments, educating girls produces the highest possible return. Besides, the benefits of improving female education go beyond increasing individual productivity and income. Reduced fertility rates ease population pressure and improve family‟s health, increase life expectancy and thus enrich the quality of life for the family as well for the community (Hill & King 1993). This analysis demonstrates that a country‟s failure to raise the education of women to levels equal to those of men imposes a substantial cost on their development efforts. However, analysis based on data of individual countries showed that even with the rise in enrolment rates, gender parity does not necessarily improve. At the same education level of male and female, the gender gap can persist. Some countries have achieved gender parity in primary school at an average enrolment rate of about 60 percent, but there are many without parity even at an average enrolment rate of 80 percent. In most cases, disparities are greater at the secondary level. Research also shows that neither a high per capita income, nor an increment in enrolment and the level of
56 education necessarily guarantee gender parity. At low levels of per capita income, female enrolment rate and parity vary the most. Even larger public expenditures on education may not necessarily translate into greater parity in enrolments. The lower attainment in schooling tends to include a number of other issues such as deprivation in health care. If the gender gap in schooling becomes large, it can be anticipated that the gap will also be relatively large in health services (P. Schultz 2002). However, the divergence between benefits and costs of girls‟ education indicates that the level of education is not at socially optimum level and thus deters the process of human capital accumulation for women themselves and thereby future generation. These costs are largely associated with low productivity, high population growth and thus higher poverty, which promulgate further into deteriorations of the economic and social systems.
2.8 Primary Education versus Post-primary Education
Primary education is indispensable for every child as it is the first step of enhancing human capability. Major objectives of this level of education are to achieve basic literacy and numeracy and to establish foundation in science, geography, history and other social sciences subjects. It also equips students with necessary skills of everyday life, knowledge of social rules and appropriate patterns of behaviour in the society. This primary education is, however, shaped according to the prevailing social and philosophical milieu and regarded as the basement for the entire superstructure of children‟s moral, spiritual, intellectual and physical developments. To become a rational human being, everybody irrespective of men and women essentially needs primary education. One of important topics in primary education is elementary mathematics. Numeracy allows people to participate in the essential economic activities, such as buying and selling goods. In the process of production, knowledge of basic mathematic is often required for workers to acquire new skills. Numeracy helps them to make better judgments and plans. For example, numerate farmers can better estimate and compare the productivity and profitability of their past products and choose the best ones to make the next year more fruitful than non-numerate ones (Kuruda 2009). The people who have accomplished primary education are able to access information on health care and public
57 services with the likelihood of sending their own children to school (Kuruda 2009). However, primary education promotes efficiency as well as equity in the society as a result of a growing tendency for redistributing income from the rich to the poor (Psacharopoulos 1985)
In the process of human capital development, the stage of secondary education follows the primary level. It provides students with both academic as well as vocational skills. The importance of secondary vocational education has gradually gained popularity (Haddad et al. 1990). It is observed that policy makers want to prepare students for practical work and to reduce pressure on higher education. Studies also confirm that vocational education has high payoffs (Komenan 1987). Based on a survey of workers on Beijing Auto Industry Company, Min and Tsang (1987) concluded that the productivity of workers having a secondary vocational education was seven per cent higher than those with a general one. Policy makers also presume that secondary vocational education is the best investment in education for economic development. However, there are many controversies relating to this type of education. Some point to the high cost involved in vocational education (Hinchliffe 1983). Reviewing the employment situation of Columbia and Tanzania, Psaharopoulos and Loxley (1985) did not find an advantage for the graduates from secondary vocational courses, in terms of success in finding jobs at equivalent pay levels in these countries. There is a disagreement about the cost- effectiveness of secondary vocational education and its impact on economic development. Although, determining an appropriate level of education for a country is difficult, a comparative analysis between primary and post-primary education provides useful insights. The relation among earnings‟ premiums and the different levels of education may provide an assessment about the education provision which a country needs to alleviate poverty or achieve a more equal income distribution (Psacharopoulos 1992). Several studies have emphasized primary education while many others revealed that the effects of post-primary education are higher compared to primary one (Curtin & Nelson 1999). The evidence from a study conducted in Five East Asian and Pacific countries (Ahuja 1997) suggested that increase of public funding for primary education achieved
58 very little in terms of poverty reduction, improvement in women‟s health and acceleration of economic growth. The study showed an inverse association between education of the household head and the level of household poverty. Primary education reduced the incidence of poverty by around ten per cent as compared with households whose heads had no formal education. In contrast, household heads with post-primary education were rarely found among the poor. In the Philippines, those with only primary education accounted for 66 per cent of the poor and in Thailand, poverty was confined to those having either no or only primary education (Curtin & Nelson 1999).
After controlling the background variables, Ainsworth, Beegle and Nayamete (1996) have shown in Sub-Saharan African countries that female primary education did not necessarily produce a significant result, while secondary education was associated universally with lower fertility even with less vigorous family planning programs. Commonly, across the world, women possessing primary education or some secondary education have lower total fertility rate (TFR) than those without education. TFR of women with primary education and of those lacking education differs insignificantly and sometimes follows an unanticipated direction. Results indicating a positive relation between primary education and the TFR cast doubt on the effectiveness of less-than- complete female primary education in lowering fertility and suggest that education does not have a depressing effect on fertility until the secondary level (Ainsworth, Kathleen & Nayamete 1996). It can reasonably be concluded that women‟s secondary education is more effective in lowering fertility especially in countries that have low levels of female secondary school enrolment. In Papua New Guinea, the infant mortality rate (IMR) was 67.6 per thousand for mothers with 5 years of primary education while the rate stood at 40.2 for mothers with secondary education (Curtin & Nelson 1999). In Bangladesh, infant mortality reduced significantly from 88 per thousand for mothers having no education to 28 when they had secondary education, it declined further to 9 per thousand with the mothers possessing higher secondary education (BBS 1998). Thus, higher levels of female education are associated with longer life expectancy, lower infant and maternal mortalities, lesser TFRs and other social benefits which imply stronger impacts of secondary and higher secondary education compared to the primary one (Gannicott & Avalos 1994).
59 Psacharopoulos (1985, 2004) summarised many studies regarding rate of returns and perceived that the returns were consistently higher in favour of primary education compared to secondary and tertiary levels of education. Jolliffe (1998) and Curtin and Nelson (1999) countered the argument relating to higher private rate of returns to education (primary-29%, secondary-18% and tertiary-20%) initiated by Psacharopoulos (1994). Curtin and Nelson (1999) debated that the rate of returns to education considered formal wages only, while in the developing countries, a significant portion of labour force was either self-employed or worked in family businesses. However, based on high rate of returns to primary school, as suggested by Psacharopoulos (1994), international aid agency, the World Bank accepted this argument in favour of concentrating public investment in the education at primary level. Curtin and Nelson (1999) later criticized this policy for misleading the achievement of a country‟s potential by shifting resources from higher to primary education. Evidence has also become prominent that post-primary education provides a larger impact than primary education on the health status of a population. Thus, the resulting misdirection of resources has significant negative impacts on education and health within the poorer sections of society. These negative consequences directly ensued from restrictions imposed by the World Bank and International Monetary Fund (IMF) in many developing countries. Conditions of World Bank and the IMF insist on the reallocation of funding from post-primary education to primary and from advanced health care to primary care. These also limit post-primary education to those who can pay full fees of private schools and colleges (Curtin and Nelson 1999). Curtin and Nelson (1999) evaluated evidence from various sources and stated that public investment in post-primary education accorded the largest pay-off per dollar in terms of the health status as well as growth rates of GNP. They also commented on donor‟s aid policies which constrained the governments of developing countries to limit public funding of education to the primary level. According to them this is an erroneous understanding of the human capital theory; because the interpretation focuses public investments‟ only on the declining marginal internal rates of return on public investments in successive levels of schooling and ignores the increasing marginal net present values of those investments (Curtin & Nelson 1999).
60
Education economists argue that more education is preferred to less, when funds are limited to the analysis of comparative marginal rate of returns (Hannum & Buchmann 2005). They suspect the analysis for two reasons. First, a given amount of money can fund more primary leavers than secondary or tertiary, as the costs per pupil of primary schooling are lower. Second, it is more equitable to educate more students than fewer (Curtin & Nelson 1999). P. Schultz (2002) points out the difficulty in estimating returns to women‟s education. As the rate of returns analysis deals with earnings (usually only wage earnings), it cannot capture the household work or the informal economic activities in which women play a significant role. Kuruda (2009) thus questioned these results regarding the impact of women‟s education on economic development. However, those countries that managed persistent growth in income had also increased investment in education and training of their labour forces irrespective of men and women. The outstanding economic records of Asian economies (Japan, Taiwan) also illustrated the importance of human capital to economic growth. Lacking natural resources, these economies grew rapidly by relying on a well-trained, educated and hard-working labour force (Becker 1993). The World Bank‟s publication „East Asian Miracle‟ (1993) suggested that one of the reasons for the successful economic development that region had high levels of human capital formation. The quantity of basic education was considerably higher in those economies than in other economies with a similar income level. The prioritized allocation of public resources to both primary and secondary education was the main determinant in the success of educational strategies in this region (Kuruda 2009).
In most developing countries, enrolments in secondary and tertiary levels of education are relatively low. Infrastructure in these respects is also limited, and cannot cover the students of entire age-groups. As a result, students drop out at a higher rate as the level of education increases. Although, there is no preset level of education required to achieve a country‟s desired development activities, secondary education has been encouraged in more recent years. As countries proceed towards development, the lengths of compulsory education typically increase to seven or eight years or longer. Hannum and Buchmann (2005) indicate that the levels of education which influence economic development depend on the countries‟ level of development. In less developed countries,
61 primary and secondary education while in more developed countries, tertiary education matters more. They state that education expansion through the secondary level appears to
be extremely important for reaping many health and demographic benefits.
2.9 Concluding Remarks
As presented, this chapter focused on the contributions of women‟s education in socioeconomic and cultural framework of developing countries. It reviewed human capital theory including its application to women‟s education. It provided analyses of economic and social benefits of their education. It described costs of educating children, the relationship between women‟s education and children‟s educational attainment, and the relationship between women‟s education and children‟s nutritional improvement. It highlighted the factors associated with low level of women‟s education and its consequences. Finally, the chapter presented arguments relating to the relative benefits of primary and post-primary education.
In articulating the chapter, relevant literature, both theory and empirical findings were evaluated in the circumstances of developing countries. Broadly, it reviewed three categories of literature. First, it examined the impact of women‟s education on their own life as well as on their children‟s welfare. Secondly, it identified educated mothers‟ role on children‟s educational attainment as well as on their nutritional status. Thirdly, it provided a discussion of contribution of literate women.
From this review, it appeared that the economic and social benefits from women‟s education make significant contributions to improving women‟s lives themselves as well as their children, although there remain controversies regarding economic benefits. Economic benefits mostly depend on the behaviour of women‟s participation in the paid labour force or how they act in that labour market. It is observed that labour force participation largely depends on women‟s own market opportunities as well as the wealth of their husbands or extended family. If the family owns greater resources, women are less likely to participate in the labour market. Again if they have more children, they tend to participate less in labour market activities. Above all, women‟s earnings depend on their own level of education as well as their intention to work for a wage. However, in
62 most cases, they participate in unpaid family businesses or in the informal sector and the impact of this work is often neglected. Generally, the increased productivity of educated women is measured by estimating the rate of returns to education based on paid labour counting only wage employment. As many women do not participate regularly in this labour market, there remain difficulties in measuring the rate of returns to women‟s education.
Among the social benefits of women‟s education are fertility reduction, children‟s improved health and nutrition as well as acquisition of higher education for the children. Reduced fertility rates associated with women‟s education significantly influence child health and nutrition. Fertility, which forms the size of family, plays a key role in maintaining the required nutrition for the children. This also helps to more equitably redistribute resources among family members by keeping family size small, which ultimately contributes to reducing child mortality. Similarly, the attainment of higher education for children has far reaching effects on society as a whole. The level of women‟s education is important not only for the present generation but also for its capacity to transmit the values, justice particularly attitudes towards girl children to the next generations. Studies also focused on the intergenerational effects on children‟s educational attainment by exploring the benefits of education, literacy distribution, positive externalities of literacy, transmission of benefits through literate person (including women) to the rest of the family as within the wider benefits of education.
An assessment of this literature strongly supports the argument that educated women bring higher levels of well being and improve their children‟s lives by utilizing their knowledge for effective health, hygiene and nutritional practice at home. They also invest more in children‟s education. Even if they do not participate in the labour market, they indirectly generate longer term economic benefits through improvements of child health and nutrition, reduction in infant and child mortality and exert efforts for higher education for their children particularly daughters. These benefits are often overlooked in the debate of the benefits for improving female education in developing countries.
It appears that women can substantially contribute to society and economic development through their perceived knowledge, understanding and cognitive skills
63 obtained from education. The contribution may exceed that of men because women are involved more intensively with health and education issues related to their children. However, despite these benefits, parents are often reluctant to sending their daughters to school, especially in developing countries. This attitude towards girls‟ education is often justified by socio-economic conditions and conservative culture, which impose restrictions on educating girls. For example, parents in many cases incur more financial costs in educating girls than boys. They also face „psychic costs‟ particularly in the rural areas. Further, the benefits of educating girls‟ are expected to be reaped by the families into which they marry. Again, in many societies due to cultural norms, boys are obligated to undertake the responsibility for father and mother in their old age which motivates parents to invest more on boys‟ education than girls. Consequently, girls acquire less education and skills and thus accumulate less human capital for themselves as well as for their children.
The focus of this thesis is to empirically investigate how women‟s education influences children‟s educational attainment and improves their nutritional status, set within the context of existing socio-economic conditions in Bangladesh. One outcome of this research will be to suggest policy guidelines for protecting and nurturing the potential productivity of future workforces through enhancing women‟s education, because a large number of children in Bangladesh with relatively low level of education suffer from malnutrition which causes a substantial wastage of human resources.
In the next chapter (Chapter 3), an overview of the education system in Bangladesh, achievements to date at various levels of education, the education policies and strategies followed by successive governments, constraints and challenges faced by the system will be presented. It will describe the overall status of women in terms of education and wages and income in Bangladesh. The chapter will also review women‟s educational status in South Asian countries such as India, Pakistan, Nepal and Sri Lanka in order to compare Bangladesh‟s educational performances in its regional context
In Chapter 4, an overview of the health system in Bangladesh, achievements to date at various health indicators, the relevant policies and strategies followed by successive governments, constraints and challenges faced by the system will be
64 presented. It will describe women‟s overall health status in Bangladesh. The chapter will also review their health status in South Asian countries such as India, Pakistan, Nepal and Sri Lanka in order to compare Bangladesh‟s health performances in its regional context.
65
Chapter 3
Education Status of Women in Bangladesh
In chapter 2, the contributions of women‟s education within the socioeconomic and cultural framework of developing countries were reviewed. This review included human capital theory, its application to women‟s education and the analyses of economic and social benefits of educated women. The chapter also described the relationships between women‟s education and children‟s educational attainment and their nutritional status. It highlighted the factors associated with low level of women‟s education and its consequences and also presented arguments relating to the relative benefits of primary and post-primary education.
The objective of this chapter is to present an overview of the education system in Bangladesh in order to evaluate the progress achieved at various levels of education. Education policies and strategies undertaken to increase primary school enrolment, retention up to complete primary school cycle and to improve quality of education are described. Constraints and challenges faced by the education system are also analysed. The chapter examines women‟s status in respect to education, employment and wage earnings. The chapter also reviews education status in South Asian countries such as India, Pakistan, Nepal and Sri Lanka to compare Bangladesh‟s educational performances in its regional context.
The chapter is organised as follows: section one presents the education system of Bangladesh while section two illustrates education policies and strategies undertaken by various governments. Section three describes the achievements to date at different levels of education. Section four illustrates the constraints and challenges faced by the education system and section five discusses women‟s overall status in the context of
66 Bangladesh. Section six compares the women‟s educational status of Bangladesh to other South Asian countries. Finally section seven concludes the chapter.
3.1 Education System in Bangladesh
The education system in colonial Bangladesh was the providence of the upper- class with all courses given in English and very little was provided to the general people. After independence in 1971, the Bangladesh Education Board reviewed these educational practices and aimed to develop education as a means of improving future prospects for all citizens. The policies and strategies undertaken by the government, importantly, emphasised women‟s education in order to improve their socio-economic status (GoB 2008c).
The education system in Bangladesh comprises three streams, being the General Education System, the Madrasah Education System (religion based) and the Technical- Vocational Education System (BANBEIS8 2006). The general education system has three-tiers. These are: i) primary level (grade I-V), ii) secondary level (grade VI-XII), which includes three stages: junior secondary level (grade VI-VIII), secondary level (grade IX-X) and higher secondary level (grade XI-XII) and iii) tertiary level (grade XIII and above). These tiers are described below as well as shown in Appendix Table: A4.3.
3.1.1 General Education In Bangladesh, primary education (grades I-V) is managed by the Ministry of Primary and Mass Education (MoPME) while secondary and higher secondary (grades VI-XII) education is the responsibility of the Ministry of Education (MoE). Tertiary levels of education, particularly universities, act as autonomous bodies performing academic as well as administrative activities. Quality issues at tertiary education are looked after by the University Grant Commission (UGC). Detail descriptions of these education systems are given below.
I. Primary School (Grades I -V) The first level of education in Bangladesh consists of 5 years of formal schooling (grades I-V) and the official age for this level ranges from six to ten years. At primary
8 Bangladesh Bureau of Education Information Statistics is a Government organization under the Ministry of Education, which supply education related data, report and update them annually on a regular basis.
67 level, the majority of schools in Bangladesh are run by the government. Around 75 percent of primary school students attend government primary schools (BBS 2006d). A considerable number of registered non-government schools, privately owned non- registered schools and Ebtedayee Madrasas (religious primary school) also operate simultaneously. The private sector has been increasing its presence in primary education since late 1970s. These schools set up as local private initiatives and managed either entirely privately or with limited financial assistance from the government. At primary school, the students generally study Bengali and English literature along with some elementary science subjects emphasising observations and the way to face daily situations (Miah 2006). The Ebtedayee Madrassas provide education with a religious focus and cover around 4.2 percent of total primary school students (BBS 2007a). In addition, several non-governmental organizations (NGOs) have set up non- formal schools to cater for children who have dropped out from the formal education system. This system of education is encouraged by the government (Asadullah 2005a). Out of total primary school students, around 3.5 percent attend NGO run schools (BBS 2007a). Further, community schools and satellite schools are run by joint partnership of government and the community. Some secondary schools (grade I-X) have primary sections started with grade one. These primary sections attached with secondary schools are known as „secondary-attached primary schools‟. English-medium or semi English-medium primary schools are collectively known as „Kindergarten‟. Students at these types of primary school can move either to Bengali medium high schools or English medium high schools which provide secondary level education. Primary schools in Bangladesh are, therefore, diversified both in terms of curriculum as well as from a management point of view (CAMPE 2009). It is worth noting that primary education has been a priority area in Bangladesh from the outset and thus the basic measures to implement universal primary education were undertaken in accordance with this priority.
II. Secondary School (Grade VI-XII) The secondary level of education comprises 7 years of formal schooling (grade VI-XII) which is administered through the collaboration of government and non- government providers within a regulatory framework. The secondary level of education
68 includes 3 years junior secondary level (VI-VIII) and 2 years of secondary level (IX-X). Higher secondary education consists of 2 years of schooling (XI-XII). At secondary level, three streams of courses are administered, being Arts and Humanities, Science and Business Education (Commerce). These courses started at grade IX after completion of junior secondary level, where students are free to choose their courses of study (BANBEIS 2009). The academic program terminates at the end of grade X and the students are to appear at the public examination called „Secondary School Certificate (SSC)‟. The Board of Intermediate and Secondary Education (BISE) conducts the SSC examinations at every year. Students succeeding at the SSC examination are enrolled at higher secondary level (grade XI-XII) with a two years course offered by Intermediate Colleges or by the intermediate section of Degree or Master‟s Colleges. This level of general education caters for students in three orientations similar to the secondary level such as Arts and Humanities, Science and Business Education (Commerce). Students face a public examination at this stage, which is known as Higher Secondary School Certificate (HSC), to qualify for higher studies at tertiary level (BANBEIS 2009).
III. Tertiary Level (Grade XII and above) The third stage of education comprises 2-6 years of formal schooling. The minimum requirement for admission into higher education is the higher secondary school certificate (HSC). Tertiary education in Bangladesh takes place at government and private universities including all subjects9 related to Arts and Humanities, Science and Business Education (Commerce). Following these three streams of orientation, students can choose either a 3-year Bachelor Degree Course followed by a 2-year Masters Degree Course or 4-year Bachelors‟ Degree Honours Course followed by 1-year Master‟s Degree (BANBEIS 2009). For those students aspiring to take M. Phil and Ph.D. courses in selected degree or areas of specialisation, the duration is of 2 years for M. Phil and 3-4 years for Ph.D. after
9 Arts and Humanity group includes Arts, Fine-arts, General History, Islamic History, Philosophy, English literature, Bengali literature, Arabic, other Religious Studies, Geography, Political science, Public Administration, Sociology, Social Welfare, Economics, Anthropology, Women Studies, Health Economics etc. Science mainly includes, Chemistry, Physics, Mathematics, Biology, Zoology, Statistics and other applied science while Commerce (marketing, accounting, BBA, management, finance) subjects.
69 completion of a Master‟s Degree. Higher education is offered in the universities and post HSC level colleges and institutes. Students can also choose professional studies such as engineering, technology, agriculture and medicine at a variety of specialised universities and colleges (BANBEIS 2010). There are 73 universities in Bangladesh. Of these, 21 universities are in the public sector, while the other 52 are in the private sector. Of the 21 public sector universities, 19 universities provide regular classroom instruction facilities and services and generally Graduate Degrees with Honours as well as Master‟s Degrees are awarded to the students in different disciplines. Bangladesh National University functions as an affiliating university for Degree and Post-Graduate Degree level education at different colleges and institutions in different fields of studies (BANBEIS 2009). After successful completion of the specified courses, final examinations are conducted and thus degree, diplomas and certificates to the successful candidates are awarded. The degrees are Bachelor of Arts (B.A), Bachelor of Social Science (B.S.S), Bachelor of Science (B.Sc), Bachelor of Commerce (B.Com), Master‟s of Atrs (M.A), Master‟s of Social Science (M.S.S), Master‟s of Science and Master‟s of Commerce (M.Com). Public universities also provide M. Phil and Ph.D degree in a selective way. Bangladesh Open University (BOU) conducts non-campus distance education programs especially in the field of teacher‟s education and offers Bachelor of Education (B.Ed) and Master of Education (M.Ed) degrees (BANBEIS 2009). Teachers‟ Training Colleges (TTCs) also offer Bachelor of Education where the minimum requirement for admission is a graduate degree. This training is provided to those who are interested for teaching profession particularly at primary and secondary school level. There is a medical university, an engineering university and an agricultural university in Bangladesh which offers Diploma and Master‟s courses in these respective disciplines. Medical college, Dental College, Nursing College and Physical Education College and Law College offer Bachelor Degrees in these areas. Other specialised institutions such as Leather Technology and Textile Engineering colleges offer four-year degree courses. However, at all levels of education, student can choose to receive their education either in English or in Bengali. Generally, Government schools use Bengali as the medium of education, however some use the English version of Secondary Education
70 Board curriculum. Some private schools use English as a medium of study following the British Curriculum (CAMPE 2009).
3.1.2 Madrasa Education
This system of education focuses on religious education, teaching all the basics of education in a religious environment. In Madrasa education, students learn Islamic religious education along with Bengali, English literature and mathematics included in general education system (Miah 2006; CAMPE 2009). The government provides grants to the teachers and employees of the non-government Madrasas. Madrasas are functionally parallel to the three major stages as the general education system: i) Ebtedayee Madrasa consists of 5 years of schooling (grades I-V), which is equivalent to primary level; ii) secondary level of Madrasa education is comprised of 7 years of formal schooling. It takes 5 years for the secondary level (grades VI-X) and 2 years for the higher secondary level (grades XI-XII). Similar to the general eduction system, three streams of courses such as Arts and Humanities, Science and Business Education (Commerce) are provided in the Madrasa education system, where students are free to choose their courses of studies. There are two public examinations namely Dakhil (secondary level) and Alim (higher secondary level), which are conducted by the Bangladesh Madrasa Education Board (BMEB) and it provides certificates to the successful students. At tertiary level, Madrasa education offers 4 years (graduate level) of formal education. The minimum requirement for admission to higher level education is the Alim certificate (equivalent to higher secondary). Alim passed students are qualified to enrol in 2-year Fazil Degree (equivalent to graduation). After successful completion of Fazil, they can enrol in a 2-year Kamil (equivalent to masters) level education. This is the highest level of education in the Madrasa education system. These two levels of examinations are also conducted by the Bangladesh Madrasa Education Board (BANBEIS 2009).
Madrasas have to follow the rules and regulations of the Bangladesh Madrasa Education Board (CAMPE 2009). Islamic teachings are compulsory and are taught in the Arabic language. Students have, however, options to move from Madrasa education to General education after completion of Alim (equivalent higher secondary level) if
71 desired. Madrasa education is mostly orientated towards male children and is also rural based (BANBEIS 2009). Moreover, students in this system need to learn two foreign languages (English and Arabic) while these institutions seriously suffer from low quality teachers as well as lack of infrastructure. Thus, the quality of Madrasa education is often debated by the policy makers, researchers and the beneficiaries themselves due to its weaker linkages to the labour market.
3.1.3 Technical and Vocational Education This system of education serves young males and females who have completed at least eighth grade of school. It provides technical education to the students and offers courses related to various applied and practical areas of science, technology and engineering. The duration of courses ranges from one year to four years including higher secondary and tertiary levels. These students whose interests are not strictly academic may find technical-vocational programs which are more valuable for their future. The course curriculum is also designed to address the needs of the job market (BANBEIS 2009). Recently, 2-year vocational courses have been introduced at the higher secondary level in government managed vocational training institutes (VTIs). There is a technical education board called the Bangladesh Technical Education Board (BTEB), which grants technical education and awards certificates to the successful candidates. At secondary and higher secondary levels of technical education, girls‟ participation has on average ranged from 20 to 25 percent.
3.1.4 Non-formal Education In Bangladesh, the non-formal education system at primary level was started in the mid-1980s, spearheaded by non-governmental organisations (NGOs) as it is difficult for the government alone to handle the large number of primary school students. The NGO-run non-formal schools cater mainly to drop-outs from the government and non- government primary schools. On completion of two to three years non-formal primary education, students normally re-enter the government/non-government primary schools at higher classes. The largest NGO program, introduced by Bangladesh Rural Advancement Committee (BRAC), operates 34,000 schools all over Bangladesh and provides the five- year cycle of primary education in four years. More than one million students are provided with BRAC school education annually, among which 70 percent are girls (GoB
72 2009b). The program emphasises girls from rural areas giving them the opportunity to attain higher levels of education by providing flexible learning hours. It also provides scholarships to the students depending on their performances. Other NGOs such as Proshika and Dhaka Ahsania Mission are also working in the field of non-formal education (GoB 2009b).
Among the above mentioned systems of education, Technical and Vocational Education is comparatively popular due to its stronger relation with the formal labour market while this relationship is weaker in the cases of General Education and Madrasa Education. Thus, graduates from these two systems of education often face severe problems in being employed in the labour market. There is, therefore, an urgent need to modernise the overall education system particularly Madrasa Education, to establish stronger linkages with labour market (ILO Report 2009). The following section describes education policies and strategies undertaken in Bangladesh in order to develop overall educational attainment.
3.2 Education Policies and Strategies Article 17 of the Constitution of the People's Republic of Bangladesh 1972 stated that all children aged six to ten years must receive a basic education without any charge. The State shall adopt effective measures for the purpose of establishing a uniform mass- oriented and universal system of education to educate its entire population. In order to comply with these constitutional obligations, successive governments of Bangladesh have been pursuing efforts to achieve universal education at the national level as well as seeking opportunities from the international arena. Bangladesh, thus, committed to undertake action to achieve „Education For All (EFA)‟ as follow up action from the global conference held in Jomtien in Thailand in 1990. It endorsed the Dakar Framework for Action 2000 (UNESCO 2000), at the World Education Forum in Dakar in 2000 by adopting six goals and twelve strategies for the development of education (CAMPE 2003). However, the Dakar World Education Forum (2000) reviewed the progress on „Education For All‟ and noted, enrolment rate at primary school had been achieved in Bangladesh during the 1990s. In addition, Bangladesh has ratified the Millennium Development Goals (MDGs) 2000 at the United Nations Millennium Summit 2000 and committed to achieving the
73 MDGs by 2015 (Goals and Targets at Global level are shown in Appendix Table A5.3). These goals are included in the country‟s two successive National Strategy Papers known as: „Unlocking the Potential: National Strategy for Accelerated Poverty Reduction (NSAPR I)‟ for the period of 2005-2008 and „Moving Ahead: National Strategy for Accelerated Poverty Reduction (NSAPR II)‟ for the period of 2009-2011 (GoB 2005b; GoB 2008c).
3.2.1 Major Policies regarding Education
The focus of polices and strategies is to develop human resource through interventions on expanding access to education and improving quality of education. The policy10 guidelines delineated in the national planning documents have great significance in developing the education system. Major policies undertaken in education sector are as follows. Free and compulsory primary education for all children according to Primary Education (Compulsory) Act of 1990 (BANBEIS 2009); Free education for girls only up to higher secondary level (grade XII); Stipend program for girls only up to higher secondary level (grade XII); Food-for-education for children from poorer families (recently food assistance has been substituted by cash assistance); At primary school recruitment, 60 per cent teachers must be female; Recruitment of teachers in primary and secondary schools based on merit; Creation of space for the private sector and NGOs to act in the area of non- formal education (CAMPE 2002). These policies proved very successful in promoting enrolment, particularly girls' enrolment at both primary and secondary schools (CAMPE 2009). 3.2.2 Strategies Regarding Education In order to sustain the above mentioned policies‟ successes, several specific strategies were also included in the National Strategy Papers. Major education strategies are given as follows:
10 It is worthwhile to note that policy offers some guidelines to achieve a certain goal while strategy indicates the ways among the alternatives how to achieve that goal. Strategy however shows a number of specific action to be implemented for achieving a goal in a certain period of time
74 Introduce early childhood and pre-school education including uniform and common primary education opportunity for all children; Increase access to primary education as well as secondary education particularly for the poor; Improve quality at all levels of education; Ensure a gender balanced approach in the formulation of the school curriculum; Increase enrolment, attendance and completion rates among students of poor families; Expand technical and vocational education (TVET) and training for eligible participants including females; Provide knowledge and skills linked to the domestic and the international labour markets; Strengthen and increase efficiency of public sector higher education; Expand non-formal education particularly for the extreme poor and people live in remote areas; and Strengthen governance and efficiency in education sector.
Above stated strategies undertaken in education sector guided by policies are well recognised and also successful in supporting the policies to be implemented. These are reflected in increased primary school enrolment particularly girls' enrolment and already achieved gender balances both at primary and secondary education.
3.2.3 Programs Implemented in Education Sector With a view to implementing policies and strategies, a number of programs are undertaken (GoB 2009a). These programs mainly involved in construction of new class- rooms, teachers‟ training, providing stipends to poor students at primary school, free distribution of text books for primary and secondary levels, nutritious food supplements for poorer students, establishment of satellite and less expensive community schools, introduction of innovative teaching methods, monitoring learning achievement and recruitment of qualified teachers (GoB 2009b). These programs are being implemented in a continuous basis and some successful programs are reviewed below.
75 I. Stipend Program for Primary School Students (SPPS)
The government of Bangladesh has been implementing the „Primary Education Stipend Project‟ for ensuring universal free primary education for boys and girls particularly in the rural areas. Incentives for all children to attend primary school have been provided through the provision of a stipend of Taka 100 for a child and Taka 125 for more than one child in school per family per month, targeted at 45-90 percent of the students in a school identified as poor. The cash stipend was introduced in 2002 which replaced "Food for Education" in the form of a monthly grain ration targeted for poor children initiated in 1993. Students of distressed female-headed families, day labourers, and landless families are the beneficiaries of this project. This project has contributed significantly to increased enrolment at primary education (GoB 2010a). However, the primary school stipend program is for both girls and boys, while at the secondary school, stipend is provided only to girls.
II. Primary Education Development Program (PEDP) This program was implemented to ensure primary education for all children since 1998. The second phase of this program is being implemented for the period of 2003- 2011. Major activities of this program are: creation of new posts for primary school teachers, free distribution of text books to government and non-government primary school, provision of improved teachers training, construction of class rooms and toilets and supply of arsenic free tube wells to primary schools (GoB 2009b). The program aims to cover all government primary education institutions in Bangladesh to increase access, quality and efficiency across the region (BANBEIS and MOWCA 2005). Under the second phase of PEDP II, enrolment in primary school has increased substantially, targeted students at primary school are provided stipend. All students from grade 1 to grade 5 are provided text books within January. A good number of teachers are also recruited under the program. For continuing the activities of PEDP, the third phase is being implemented from July 2011.
III. Reaching Out-of School Children Project (ROSC) To achieve the goal of „Education for All‟, the government of Bangladesh is implementing the „Reaching Out-of School Children‟ project. This project aims at
76 ensuring primary education for „out of school‟ children aged 7-14 years in 60 disadvantaged thanas (sub-districts). Children who had never enrolled in formal school or had dropped out from school have been targeted to enrol in learning centres to bring them back into the primary education system. This is the first government initiative to provide complementary non-formal primary education for out of school rural children (GoB 2010a). IV. School Feeding Program (SFP) In order to create an enabling environment for education, „School Feeding Program‟ is being implemented with the support of World Food Program in highly food- deficit areas. Under this program, 75gm fortified biscuits are supplied to students at primary school in order to motivate children to attend school as well as supplementing their nutritional requirements (GoB 2008b). This program is being implementing since 2006 in a limited poverty prone area to increase children‟s school attendance.
V. Female Secondary School Stipend Program (FSSP) The nation wide Female Secondary School Stipend Program was launched at secondary level from January 1994 in order to empower women and enhance their socio- economic status through expansion of their access to education. The government has waived tuition fees of female stipend-holders up to twelfth grade and also, they are awarded a monthly stipend for attending school regularly (GoB 2009a). Remarkable progress has been achieved in girls‟ secondary school enrolment due to introduction of this stipend program for the girls particularly in the rural areas (GoB 2010a).
Due to prevailing various school system in Bangladesh no uniform tuition fee charged to students at school level except government owned school. Government owned primary schools are running free of tuition fee for both boys and girls. At secondary level, female students particularly in the rural areas are waived tuition fees and also additionally have some financial support on monthly basis. While boys do not receive this support and additionally they have to pay tuition fees for attending school. VI. Secondary School Sector Improvement Program (SESIP) In order to increase access and quality improvement, under this project, construction of schools, residences particularly for female teachers, introduction of new curriculum and a new system of public examination were undertaken through out the
77 country at secondary level (GoB 2009a). This project started middle of the 1990s and basically emphasised creating infrastructure facilities particularly in the remote areas. This program is proved as very effective to expand secondary school enrolment which is
still low compared to the international standard. VII. Compulsory Primary Education Implementation Monitoring Unit With the enactment of the Primary Education (Compulsory) Act of 1990, the Government created the Compulsory Primary Education Implementation Monitoring Unit in 1991, headed by the Director-General was established. This Unit has responsibility to monitor compulsory primary education program at field level and conduct surveys to collect information on the numbers of the primary school-aged population and of children attending schools (BANBEIS 2009). Thus, the unit plays an important role in monitoring the progress on primary school and also provides primary school related information to the different users. VIII. Total Literacy Movement (TLM) The government launched a Total Literacy Movement (TLM) in 1994 to gradually free the country from illiteracy. A Directorate was established in 1995 as a permanent set up for non-formal education (GoB 2009b) with the responsibility of executing policy decisions and plans relating to non-formal education through i) NGO- run schools and ii) total literacy movement by the district/thana administration. The total literacy movement program was developed as a six-month campaign to eradicate illiteracy from a district. The program was financed entirely by the government and administered by the district administration. Owing to this program, the government has so far declared six districts free of illiteracy. There is, however, wide spread speculation about the authenticity of this claim (CAMPE 2003). Currently two government programs, namely Post Literacy and Continuing Education for Human Development (PLCEHD) and Basic Education for Hard to Reach Working Children (BEHRWC) are in operation to address the education needs of adults and hard to reach working children (GoB 2009b). The targeted school interventions in impoverished regions should considerably reduce the „out of school‟ child population. The Government of Bangladesh has operated several income assistance programs such as Vulnerable Group Feeding, Vulnerable Group Development. In Bangladesh, primary school enrolment is significantly associated
78 with these development programs (GoB 2009b). The education policies stressed early childhood education prior to starting primary school education and also emphasised technical and vocational education. The non-formal education system plays a considerable part in covering the school drop-out group, the hard to reach group, and illiterate adults in the society. Private sector contributes increasingly to the education field and contributions are also made by the NGOs particularly in non-formal education.
3.2.4 Financial Expenditure The successive governments of Bangladesh have increasingly placed more emphasis on investment in the social sector, especially on education and health areas as the basis of human resource development and women empowerment. In order to improve the quality of education, recently, the government has increased its education budget aiming to provide more facilities such as computers, text books, laboratory equipments (GoB 2009b). For the last several years, the education sector has received the highest budgetary allocation among all sectors. Within intra-sector allocation, the primary education sub-sector receives around half of the education sector budget followed by the secondary education which receives around one third of total budget. Budget allocations to the education sector from Fiscal Year (FY) 1991-92 to FY2007-08 (GoB 2009a) are shown in Table 3.1. Table 3.1: Government Budget on Education from 1990-91 to 2007-08
Fiscal Year Budget Percentage changed (%) (Taka in million) 1990-91 14944 - 1995-96 35226 135.72 2000-01 58517 66.12 2001-02 58776 0.44 2002-03 65038 10.65 2003-04 67579 3.90 2004-05 71301 5.50 2005-06 94877 27.66 2006-2007 107219 17.79 2007-2008 114544 6.83 Source: Bangladesh Bureau of Educational Information System (BANBEIS) 2010, Ministry of Education. www/banbeis.gov.bd/db_bb/education_finance1.html
79 Table 3.1 presents the average growth in budget allocation in education sector, which increased significantly (around 136 per cent) from FY1990-91 to FY1995-96. Similarly, the average budget in education sector increased 66 per cent in FY2000-01 from the period of FY1995-96. Since FY2001-02, the allocation grew steadily until FY2004-05. Again, the allocation increased around 28 per cent in FY2005-06 from the growth rate 5.5 per cent of previous year (FY2004-05). The annual growth of FY2006-07 and FY2007- 08 were 17.79 and 6.83 per cent respectively. Although, the annual budget allocation has increased over these years in Bangladesh, it is still low compared to other South Asian countries. The annual budget dedicated for education is around 2.40 per cent of the country‟s GDP, compared to the regional average of 3.5 per cent of South Asian Countries. This proportion needs to be enhanced four to five per cent to achieve the education related MDG targets (CAMPE 2009). Achievements at various levels of education are discussed in the following section.
3.3 Achievements at Various Education Levels
Bangladesh has achieved substantial progress in primary school enrolment during the last decades. The net primary enrolment rate11, which was around 60 per cent in 1990, increased to 91 per cent in 2008. In the five-year primary school cycle, the completion rate has increased from 40 per cent in 1990 to 61 per cent in 2008. Girls‟ enrolment at primary school increased to 93 per cent in 2008 from 51 per cent in 1990. This indicates that considerable progress has been made in increasing primary school enrolment particularly for girls. As Bangladesh is one of the signatories to attaining the Millennium Development Goals (MDGs) set by UNDP in the Millennium Summit 2000, progress in the education sector is discussed in terms of achievements of MDGs. National level education goals and targets were set to be consistent with MDGs (as shown in Appendix Table A3.3) and thus attempts are being made to attain those goals and targets by 2015. Bangladesh is on track to achieving its target of MDG-2 regarding universal primary school enrolment. It has already achieved its target of MDG-3 regarding gender parity in both primary and
11 Net Enrolment Rate refers to the number of pupils in the official school age group 6 to 10 years, in a given school year, expressed as percentage of the corresponding population of eligible official age group (GoB 2009).
80 secondary education levels (GoB 2007). It is, however, behind scheduled on achieving its target of MDG-2 regarding completion of 5-year primary school cycle as well as target of MDG-3 with regard to gender parity at tertiary level. The following sections describe this progress at different levels of education. 3.3.1 Primary education From the outset, the primary education sub-sector in Bangladesh has received the highest budget allocation. Primary education was made compulsory in 1990 and the other measures to increase enrolment and the completion of five-year primary school cycle up to a standard level were introduced. Adequate physical facilities were also created to make these programs successful (GoB 2007). As a result, net enrolment reached 91 per cent in 2008 from 60.5 per cent in 1990 (BANBEIS 2009). Primary school enrolments during 1990 to 2008 are presented in Table 3.2.
Table 3.2: Primary School Enrolment Rate during 1990-2008
Sex/Year 1990 1995 2000 2005 2006 2007 2008 Both Sex 60.5 75.75 85.5 87.0 87 89 91 As % of Girls 50.76 73.86 85.8 90.0 89.3 91.8 93 Source: Bangladesh Bureau of Educational Information System (BANBEIS) 2006 Ministry of Education and Government of Bangladesh.
Table 3.2 shows that the primary school enrolment rate at the national level increased from 60.5 per cent in 1990 to 75.75 per cent in 1995, it further increased to 85.5 per cent in 2000. This indicates that enrolment has increased at a higher rate during the period of 1990-2000 and the rate became stagnant since 2000 and onward. During the same period from 1990 to 2008, girls‟ enrolment rate has also increased substantially and the rate was comparatively higher than the overall progress. Primary school enrolment from 1990 to 2008 is shown in Figure 3.1.
81
Figure 3.1: Primary School Enrolment Trend: 1991 - 2008
Enrollment at primary level
120
100 100 90.1 85.585.8 87.2 80 73.8675.75
60 60.5 50.76 40
20
0 1990 1995 2000 2005 2015
Both Sex As % of Girls
Source: Government of Bangladesh (GoB) 2007, Progress in Achieving Millennium Development Goals (MDGs) 2007’, GED, Planning Commission.
Figure 3.1 shows an impressive progress was made in primary school enrolment during the period of 1990-2000, while from the 2000 onward, the enrolment rate increased at a decreasing rate. One of the targets of MDGs is to achieve 100 per cent primary school enrolment by 2015, if this trend continues, it may not possible to attain this target. It is found from the experiences of other countries that once a country attains net enrolment above 90 per cent, it becomes very difficult to reach the last 10 per cent of the eligible students who are severely disadvantaged in terms of location, economic and social condition or due to may be ethnic minority (GoB 2010b).
3.3.2 Secondary Education Secondary school enrolment has also improved since 1990 although the pace of improvement was not maintained at this level as it was at the primary level. The enrolment rate at secondary education was 47 per cent in 2008 as against around 30 per cent in 1990 (BANBEIS 2010). The drop out rate is higher at the secondary level compared to primary school. However, the share of boys and girls enrolled in secondary school is presented in Table 3.3.
82 Table 3.3: Share of Boys and Girls enrolled in Secondary School during 1990-2008 Sex/Year 1990 1995 2000 2005 2006 2007 2008 Boys 66 43 47.4 47.7 47.7 47.8 46.3 Girls 34 47 52.6 52.3 52.3 52.2 53.7 Source: Bangladesh Bureau of Educational Information System (BANBEIS) 2010, Ministry of Education; GoB 2010b.
Table 3.3 shows that girls‟ share in secondary school increased to around 54 per cent in 2008 from 34 per cent in 1990. A great stride was made from 1990 to 1995 and the share became stable around 52-54 per cent between 2000 and 2008. Similarly, the share of boys and girls enrolled in higher secondary school increased during the period of 1990s and 2000s. This improvement has taken place due to implementing a large scale decade long „Female Secondary School Stipend Program‟ along with free education for girls‟ up to grade XII (as discussed in 3.2.3). Despite this progress, the majority of children of the secondary school age group (11-15 years) remain out of school. Within the 47 per cent of gross enrolment rate at secondary school, there is a substantial decline from the junior to the higher secondary level. The gross enrolment rate was 31.4 per cent at the junior school level, 43.5 per cent at the secondary school (GoB 2010b). However, at higher secondary level, a great transition has been taken place due to a considerable number of students not succeeding at the SSC examination. Nearly one third of the students drop out before completing grade 5, and roughly one in five students who enrolled in grade six pass the Secondary School Certificate examination and one in ten obtain the Higher Secondary Certificate (GoB 2010b). 3.3.3 Tertiary Education Students enrolled at the tertiary level is low due to the low survival rate at secondary and higher secondary education. Further, tertiary education involves higher expenditure compared to primary and secondary levels. Boys and girls‟ share in enrolment major faculties of public universities are shown in Table 3.4.
83 Table 3.4: Share of Boys and Girls enrolled in Pulic Universities: 1990-2008
Sex/Year 1990 1995 2000 2005 2006 2007 2008 Share of Boys 78 77 76 74.7 74.6 74.8 76 Share of Girls 22 23 24 25.3 25 24.5 24
Source: Reports of Bangladesh Bureau of Educational Information System (BANBEIS) 2006, 2010 Ministry of Education, Government of Bangladesh.
Table 3.4 shows that girls‟ share in major faculties of public universities presents roughly one fourth of total students enrolled. Although girls‟ share increased to 24 per cent in 2008 from 22 per cent in 1990 but it is almost stable to the range of one fourth of boys‟ share over the two decades. Girls‟ participation in private universities has also shown similar pattern and ranges from 20-27 percent of total enrolled students during the same period (BANBEIS 2010).
3.3.4 Adult Literacy Rate The literacy rate (15 years and above) increased to 58 per cent in 2008 from 37 per cent in 1990 (GoB 2005b). Literacy is measured based on the UNESCO criterion of whether a person can „with understanding both read and write a short, simple statement on his/her everyday life‟. Due to several measures undertaken by successive governments (discussed in section 3.2), female‟s share in primary education has increased to 50:50 in 2008 from 45:55 in 1990. The scenario is also impressive in secondary education where the ratio increased to 55:45 in 2008 from 34:66 during 1990 (GoB 2010b). Although progress has been made in primary school enrolment, the literacy rate has not been maintaining the similar pace. Table 3.5 presents the literacy rate from 1990 to 2008.
[
Table 3.5: Literacy Rate (15 years +) during 1990-2008
Sex/Year 1990 1995 2000 2005 2006 2007 2008 Both 37.0 45.3 52.8 52.3 52.5 53.5 58 Male 44.3 55.6 61.0 57.6 57.6 58.7 63 Female 25.8 38.1 43.2 47.9 48 48 49 Source: Report on Sample Vital Registration Survey, 2006 (SVRS), Bangladesh Bureau of Statistics, Ministry of Planning Government of Bangladesh; GoB 2010b, HDR 2010, UNDP.
Table 3.5 shows that the literacy rate in Bangladesh increased from 37 percent in 1990 to 45 per cent in 1995 and it further increased to 53 per cent in 2000. The rate ranges from
84 52 to 58 per cent between 2000 and 2008 which implies that literacy rate remains stagnant during this period. This also indicates that primary school completion rate is not increased as it is expected. Importantly, although the female literacy rate increased from 26 per cent in 1990 to 43 per cent in 2000 and 49 percent in 2008, still a considerable difference persists between male and female literacy rate. However, the literacy rate during 1990 to 2008 is presented in Figure 3.2.
Figure 3.2: Literacy Trend during the period of 1990-2008
Adult Literacy rate
120
100 100
80
60 61 57.2 55.6 52.8 51.6 44.3 45.3 43.2 45.8 40 37 38.1 25.8 20
0 1990 1995 2000 2005 2015
Both Sex Male Female
Source: Progress on Achieving towards MDG 2007, Bangladesh, General Economics Division, Planning Commission.
Figure 3.2 shows that the adult literacy rate increased from 1990 to 2008 but the rate is increasing at a slower rate. Based on the year wise literacy rate, it would not be possible to attain MDG target of 100 per cent literacy at national level by 2015 (GoB 2010b). However, although literacy rate in Bangladesh has increased over the years, it is not only far below the expected level (CAMPE 2009) but also remains stagnant since 2000 and onward. In order to increase adult literacy rate it requires continuing the existing ongoing education policies and programs in a more coordinated approach. Nonetheless, some of challenges faced by the education sector and are discussed in the following section.
85 3.4 Constraints and Challenges in Education Sector Bangladesh is currently running one of the largest (around 20 million) primary education sectors in the world. The organisational capacity needed to manage such a huge primary education students is a big challenge for the country like Bangladesh (CAMPE 2009). Therefore, although access to basic education is a fundamental human right for every citizen, this fundamental right has been denied to a large section of the population living below the poverty line. The education sector in Bangladesh faces several constraints and challenges which include very low quality education at all levels, high drop out rates and repetition of grades and problems in reaching out of school children. The sector also faces serious shortage of class rooms, shortage of qualified teachers, lack of proper teaching aids, lack of awareness and interest of parents, weak governance, pervasive rates of corruption and pilferage along with lack of resources (CAMPE 2003). Further, there is a serious mismatch in demand and supply of skilled labour as produced by the education system (ILO Report 2009). Other emerging issues are: diversified curriculum particularly in different primary school settings, low quality of education at all levels particularly in the rural areas and unsustainably high expenditure for education (GoB 2009b) particularly secondary and above education. These major problems are discussed below.
3.4.1 ‘Out of Reach’ School and Dropped out Children In Bangladesh, on average, about 20 million primary school aged-children every year nominally available to go school. Of these, around four million children remain out of school and another four million or more drop out before completing primary education (CAMPE 2009). School enrolment rates fall drastically from primary to secondary level. An argument put forward in the Daily Prothom Alo, (2 December 2009, p1) referring to the study by CAPME (2009) mentioned that nearly half of the students dropped out before completing grade 5. It also reported that only 20 per cent of students enrolled in grade I is expected to appear for the public examination for the Secondary School Certificate (SSC). The retention rate for SSC is very low and thus a serious concern from the human capital perspective. Moreover, the education system is still largely inequitable for the children from disadvantaged backgrounds and also most adversely affected. Although primary education (grades I-V) is compulsory for every child in Bangladesh, a
86 considerable number of children still remain out of school. The main cause of high drop out at primary school is due to the high prevalence of poverty, which affects one third of total Bangladeshi population.
3.4.2 Quality of Education
In Bangladesh, quality of education is a matter of serious concern. There are major gaps in the concept and knowledge of early childhood development and activities supporting young children‟s mental development, posing considerable lost opportunities to develop the full potential of the child. Although, the dominant role of primary education is to acquire literacy however, the low literacy rate demonstrates that the past investment was very poor and thus the quality of primary education in Bangladesh (GoB 2008) remains very low. Madrasas are particularly lagging behind in terms of quality education as these institutions seriously lack trained teachers, use separate textbooks and the majority of schools lack basic minimum infrastructure and learning facilities (CAMPE 2009). As a result, two third of the students who complete primary education cycle would likely to remain illiterate (CAMPE 2003). A study by Education Watch 2008, using a learning achievement test and a household survey showed that although improvement occurred in primary school attendance, progress has been rather slow in quality improvement (CAMPE 2009). The problems associated with quality education are as follows:
On average, one third of those who enter primary education do not complete the five-year cycle; Most students take on average 6 years and more to complete the 5-year primary school cycle; Teacher-pupil ratio is 1:45 which is well above with international standard of 1:25 (BANBEIS 2009); The teaching-learning process tends to be didactic, focusing on memorizing information rather than on developing analytical skill; Hours spent on meaningful teaching-leaning activities are considerably low; Academic supervision and inspection are extremely inadequate; and
87 Data and information regarding education are insufficient to monitor the progress of educational achievement (CAMPE 2009).
Factors such as lack of library and laboratory facilities or very little use of these facilities and lack of academic supervision, which are essential to improve the quality of education are often absent at all levels of school (CAMPE 2009). As a result, only a very few of those students who complete the 5 year primary school cycle are thought to achieve minimum basic education criteria. It has also been found that literacy courses, including the government run Total Literacy Movement (TLM) in Bangladesh had no significant impact on the literacy situation (CAMPE 2003).
3.4.3 Rural-Urban Difference
There is a significant difference in educational attainment between rural and urban areas. A study by Education Watch 2002 showed that rural people are 26 percentage points disadvantaged compared to their urban counterparts in terms of cognitive achievement and infrastructure facilities. The urban slum dwellers fare the worst with a literacy rate of 19.7 per cent against the national average of 41.4 per cent computed by the CAMPE using its own definition and data. The study underscored the need for a broader and integrated vision for non-formal and continuing education programs (CAMPE 2003). The World Bank‟s MDG Report (2005) stated that although Bangladesh has achieved a rapid progress in expanding primary school enrolment during the last two decades, it has large rural-urban difference in educational attainment. A study on differential performance in secondary education in Bangladesh (HCL 2007) suggested that it needs immediate declaration for secondary education as „universal education‟ due to upcoming probable threats to the survival of low quality secondary education particularly in rural areas. The study (HCL 2007) also recommended for continuing the stipend program for disadvantaged children as they might excluded from the education system.
3.4.4 Educational Expenses In Bangladesh, a significant number of people are living below the poverty line. Expenses needed to educate children are a heavy burden particularly to the poorer families. Educational expenses include private tuition fees, costs for books and
88 stationaries, transport costs, costs for uniforms and shoes etc. It also includes the opportunity costs of child labour. Further, total expenses for education increase rapidly as the level of education increases. The report of Education Watch (2002) showed that annual expenditure per student at primary level was around US$ 20 (CAMPE 2003). Moreover, primary education or lower secondary education does not significantly help these children to accommodate in the labour market. Therefore, even if primary education is tuition fee free, other educational expenses and lack of opportunity to be employed in the paid labour market discourage the poorer families from continuing education for their children (ILO report 2009).
3.4.5 Poor Governance As a major thrust of human resource development, the education sector has received the highest budget allocation for several years. The sector is also implementing a number of projects and programs regarding expansion of enrolment as well as quality improvement (GoB 2009). However, unexpectedly, very poor governance in operating academic activities as well as development programs and projects seriously undermine the success achieved by the education sector. School management committees are fully rife with politics. Teachers‟ recruitment and selecting the beneficiaries for providing stipends are often subject to personal influence. Moreover, teacher absenteeism is rampant and they often place greater emphasis on private tutoring rather than on teaching in classrooms (CAMPE 2009). There have been numerous text book production and procurement scandals over the years and the text books that are supposed to be distributed to the student for free are in the markets for sale. These types of governance problems largely contribute to the poor quality of education in Bangladesh and undermine the tremendous gains obtained from expanding access to primary school over the past decade (World Bank 2005).
3.4.6 Lack of Resources The total number of students at primary school in Bangladesh is around 20 million, one of the largest primary education systems in the world (CAMPE 2009). To manage primary education for this eligible age group requires substantial resources. One of the major constraints for expansion of quality education in Bangladesh is the lack of
89 resources as well as effective use of those resources that are available. The successes so far achieved in education are mostly in quantitative terms such as enrolment and attendance in classes, quality of education, however, is still a major concern. Owing to the high drop out rate as well as repetition across all the primary classes, completion rates of primary school is much lower. High drop out rates also result in a lack of quality achievements, waste of resources and thus inefficiency in the system. There is a significant wastage of resources taking place in the primary education system (CAMPE 2009). The report of Education Watch 2002 stated that the quantitative gain is being blurred by the slow progress being made in the quality of learning (CAMPE 2003). The report also argued that the unacceptably poor quality of teaching and learning is the biggest challenge to improving the quality of education. Massive improvement is required in classroom teaching skills and teacher‟s development. Long term economic development will be difficult if human resources are not developed properly. A strong political commitment for a major overhaul in the education system is thus required (CAMPE 2009). Although, in Bangladesh, girls‟ primary and secondary school enrolment increased considerably, drop out rates are higher for girls after grade VI and far, fewer 16.7 percent girls complete grade X than do 23.5 percent boys (BBS & UNICEF 2009). In 2008, national assessment of pupils in grades III and V, boys performed better than girls‟. In an assessment of learning achievements of students in grade X, the report of Education Watch 2008 found that boys demonstrated significantly better performance than girls in all types of secondary institutions, irrespective of public or private, urban or rural schools. Inequity and problems related to non-uniformity among the different systems of education are major challenges. Other factors such as expenditure on education and distance between home and school particularly in rural areas also influence school enrolment. Improved roads and transportation have major implications for enrolment rates especially for girls‟ enrolment and attendance rates. Many government schools are too far away or too crowded, discouraging parents from sending their children, particularly girls, to schools in the rural areas (GoB 2009b). Student‟s achievement of nationally determined competencies has improved but it is far below expectation. Low
90 achievements in the „understanding level‟ and inequities in terms of gender, school type and residence are some related issues linked to the quality of the system. Students learning achievement, however, depend substantially on their background characteristics and private tutoring (CAMPE 2009). While girls are ahead of the boys in enrolment, their learning achievement is significantly behind. Educational achievements are significantly influenced by family background factors such as household income, parent‟s level of education, expenditure of schooling, teacher‟s qualification, transport facilities, distance to schools and supply of electricity at the village (GoB 2009b). Within the system of education while Bangladesh is achieving well in terms of increasing overall enrolment rates and gender equity at primary and secondary education, the actual quality of education being provided is not improving. Quantitative improvement in enrolment is a necessary condition though, not sufficient condition for achieving improved educational outcomes and human capital for children. Moreover, completion of 5-year primary cycle is necessary for a person to be classified as literate which has not been increased as expected. In consistent with this, adult literacy rate has also remained stagnant over the last few years. Keeping these issues together this thesis used level wise primary and secondary school attendance (which is a proxy of educational attainment) data to assess the children's educational attainment particularly focusing on mother‟s education. In the context of education policies and strategies undertaken by successive governments, particularly women's education and empowerment, the overall women‟s status in Bangladesh is explained by the poverty status, participation in the labour market and wage earnings. These are described in the following section.
3.5 Overall Women’s Status in Bangladesh The national planning documents extensively emphasised women‟s issues over the years. National strategic goals were set in order to achieving the MDG regarding „Promoting Gender Equality and Empowering Women‟. Important strategies were undertaken such as, i) ensuring women‟s full participation in mainstream economic activities; ii) ensuring social protection for women against vulnerability and risks, and iii) enhancing women‟s participation in decision making. With a view to achieving these goals, expansion of female education has been emphasised since the early 1990s
91 including awareness raising programs. Improvement has thus been made in girls‟ education particularly at primary and secondary school enrolment and gender balance has also been achieved (GoB 2010b). Eventually, education stimulates and empowers women to enhance their personal development (CAMPE 2009) as well as to participate in the formal labour market. Women‟s political participation particularly in local government institutions (LGIs) has noticeably increased to 17.512 per cent of total participation in 2005 from 8.3 per cent in 1991 (BBS 2007b). The number of elected women parliamentarians was only 4 in the 1991 national election, which later increased to 34 through creating a constitutional provision of 30 reserved seats for women in the national assembly. This provision has been increased to 45 in 2006. As a result, the proportion of women seats in national parliament has been increased from 10.3 per cent in 1991 to 19 per cent in 2008 (GoB 2010b). However, women‟s role in political leadership remains sluggish. Females‟ participation in the teaching profession increased significantly but their numbers are still low at management levels of these institutions (CAMPE 2009). Despite these improvements, women in Bangladesh remain particularly vulnerable in terms of poverty and violence against them by husbands, employers and others. Social and cultural norms and conventions largely limit their access to economic resources such as capital, skills, and knowledge and thus restrict their participation in the labour market, political arenas and other forms of decision making processes (World Bank 2003). It is also observed that female-headed households where women are widowed, divorced or separated have considerably higher incidence of poverty relative to others. Table 3.6 presents the incidence of poverty by sex of household head.
12 This figure is shown as 2005 from Household Income and Expenditure Survey 2005, which is the latest available survey to use for analysis.
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Table 3.6: Incidence of Poverty (%) by sex of Head of Household: 2000 and 2005 Residence Hardcore Poverty13 Absolute Poverty 1850 kilo calorie/person/day 2122 kilo calorie/person/day Female headed Male headed Female headed Male headed 2000 2005 2000 2005 2000 2005 2000 2005 National 35.4 21.9 34.2 25.4 47.2 29.5 49.0 40.8 Rural 39.8 23.6 37.7 29.0 50.6 31.0 52.5 44.9 Urban 22.0 16.2 19.9 14.5 37.1 24.4 35.1 28.7 Source: HIES 2005, Preliminary Report of Household Income and Expenditure Survey 2006 BBS, Ministry of Planning, GoB.
Table 3.6 shows that the incidence of poverty was reduced for female-headed households both in terms of hardcore poverty and absolute poverty during the period of 2000 and 2005. In terms of absolute poverty, the incidence of poverty was 29.5 per cent for female- headed households while it was 40.8 per cent for male-headed households in 2005. In terms of hardcore poverty, the incidence of poverty was 21.9 percent for female-headed households as against 25.4 per cent for male-headed households in 2000. In absolute terms, poverty has reduced 17.7 per cent for the female-headed households while it reduced by 8.2 percent for the male-headed households during 2000 and 2005. However, poverty levels between households headed by males and females both in rural and urban areas improved. Women's participation in the labour market in Bangladesh has been increasing since the 1990s mainly due to educational expansion as well as economic expansion. Women are moving into the formal wage labour market, especially women from poor households. According to Labour Force Survey (LFS), women‟s labour force participation increased to 29 per cent in 2005-06 from 16 per cent in 1995-96. Data from the Bangladesh Garment Manufacturers and Exporters Association (BGMEA) indicates that the proportion of women in the garment industry increased sharply between 1991 and 1998 compared to men. It is worth noting that the garment industry is the largest wage employment provider (more than 90%) to women in Bangladesh. This is shown in Table 3.7.
13 In Bangladesh, poverty is usually measured by daily calorie consumption per person. According to Household Income and Expenditure Survey, hard core poverty is the situation when anyone cannot consume 1805 kilo calorie per day. On the other hand, absolute poverty is measured on the basis of 2122 kilo calorie. If anyone cannot consume 2122 kilo calorie in a day, he/she falls below the absolute poverty line.
93
Table 3.7: Employment Status in Garment Industries during 1991-1998 Years Male Female Total Females as % of total Employment 1991/92 8,730 494,700 582,000 85 1997/98 150,000 1,350,000 1,500,000 90 Source: Bangladesh Garment Manufacturing and Export Association cited in CPD (2001), page 322.
Table 3.7 shows that female employment measured as a percent of total employment in the garment sector increased from 85 per cent in 1991/92 to 90 per cent in 1997/98. This implies that a large proportion of the workforce in garment industry is women, which contributes around 80 per cent to the economy (CPD 2001). However, despite increased women‟s participation in the paid workforce, gender disparities are still marked in many aspects of the labour market. In Bangladesh, although the wage gap between male and female is narrowing, it is still significant. This is shown in Table 3.8.
Table 3.8: Average Female Wages during 1998 Sector Female Wages as % of Male Wage Agriculture 71 Manufacturing Average 35 RMG 66 Self-employed 37 Average across all sectors 51 Source: Developing a Policy Agenda for Bangladesh CPD, 2001.
Table 3.8 shows that women receive 51 per cent of men‟s average wage across the major sectors. The lowest wage is received in the manufacturing industries, where it is only 35 percent of men‟s average wage and the highest proportional wage received by women was 71 percent. Women work either in low-skilled, low paid jobs or they are concentrated in the informal economy. Even in the same job, wage levels are lower for women than for men. (Asian Development Bank, 2004). This may be explained by the deeply entrenched discriminatory attitudes towards women by employment providers. This is shown in Table 3.9.
94 Table 3.9: Proportion of Women in Selected Sectors of the Economy Sector Women as % of Total Workforce Total 19.2 Public/Autonomous 11.9 Formal (private) 6.2 Informal sector (private) 22.7 Non-profit Institutions 44.2
Source: Labour Force Survey 2002-03, total proportion excludes temporary and day labourer, BBS, Ministry of Planning, Government of Bangladesh.
Table 3.9 shows that women‟s participation is highest in the non-profit institutions where it is 44 percent, followed by the informal sector (private) where it is nearly 23 per cent.
Women‟s economic deprivation is reflected in their low participation in the labour market, low return to their labour and their concentration in low paid jobs. According to the Labour Force Survey2005-06, a large proportion of women (34.3 per cent) work as unpaid domestic workers compared to 6.4 per cent of men. About 27 percent of women are self-employed as against 51.6 per cent of men. A significant proportion of women remain underemployment suggesting a lack of job opportunities for women. Data presented in Table 3.10 also demonstrates that women earn significantly less than their male counterpart per month.
Table 3.10: Average Monthly Income (Taka) per Household, 2000 Female-headed Male-headed Female Headed as % of Male National 4453 5996 70 Rural 3447 4946 70 Urban 7090 10177 70 Source: HIES 2000, BBS, Ministry of Planning, Government of Bangladesh.
Table 3.10 shows that a female headed household earns on average 70 percent of her male counterpart. The average monthly income of female-headed household (FHH) in rural areas is approximately 35 per cent lower than that of a male-headed household (MHH) (HKI 1998). This corroborates the assumption that female-headed families have difficulties in receiving adequate income or benefits from the targeted programs. The perceived inferiority of women and girls is deeply embedded in Bangladeshi society. Discrimination starts from birth and persists throughout life. Many families
95 particularly in the rural areas still keep their girls from school as they believe girls do not need any education for their development (BANBEIS and MoWCA 2005). Many girls are married at very young ages, which eliminate the chance to receive further education. Although, gender parity has been achieved in primary and secondary school enrolments, girls‟ attendance still remain lower than that of boys‟ particularly in the rural areas (BBS
2006d). This puts them at an immediate disadvantage in the learning process. However, in Bangladesh, social attitudes towards women are changing over the last few years and more women are taking part in economic activities. Women‟s increased participation in the labour market allows them to take greater control over households‟ income. This contributes to increase in life expectancy, reduce fertility and improve the health of family members. Similarly, girls‟ increased enrolment in primary and secondary school indicates a significant change has been taking place in family attitudes towards girls. The government‟s increased recognition of women‟s human rights and educational expansion have contributed to these attitudinal changes (GoB 2010b). The following section describes the women‟s education status of Bangladesh with its regional context.
3.6 Comparison among South Asian Countries Bangladesh, India, Nepal, Pakistan and Sri Lanka are the countries of South Asia that have been linked by age-old cultural, social and historical traditions and these have enriched the interaction of ideas, values, cultures and philosophies among the people of this area. These commonalities justify the inclusion of the above countries in this study, although they differ in terms of per capita income, literacy and socio-economic status. Sri Lanka, an island country has performed exceptionally well in educational attainment for both men and women. It also scores high in gender equality and on the human development index (HDI). It is worth noting that HDI statistics released by the United Nations Development Programme is based on health, education and income statistics. It classifies the countries into three levels: „low human development‟ indicates the country which scores HDI between 0.0 and 0.5, „medium human development‟ country scores HDI between 0.5 and 0.8 and „high human development‟ country scores between 0.8 and 1.0. The following Table presents a comparative picture of economic performances among the South Asian countries.
96
Table 3.11: Key Economic Indicators: Bangladesh, India, Nepal, Pakistan and Sri Lanka, 2009 Country/indicators Bangladesh India Nepal Pakistan Sri Lanka
Per capita Gross Domestic 551 1192 427 995 2068 Product ( current US $) GDP growth (%) 5.7 9.1 4.7 3.6 3.5
Inflation: Consumer Price 5.4 10.9 11.6 13.6 3.5 Index (%) Contribution of Agriculture (% 19 18 34 22 13 of GDP) Population Size (in million) 162.0 1155.3 29.3 169.7 20.3
Population Growth (%) 1.4 1.3 1.8 2.1 0.7
Urban Population (% of total 28 30 18 37 15 population) Human Development Value 0.463 0.512 0.423 0.487 0.653 Index (HDI) Rank 129 119 138 125 91
Source: Country Profile, World Bank 2011; HDR 2010, UNDP.
Table 3.11 shows that per capita gross domestic product (GDP) in Bangladesh was US$ 551 in 2009 and the growth rate of GDP was 5.7 per cent, indicating moderate economic growth. The contribution of agriculture in the economy was 19 per cent in 2009. Bangladesh is a densely populated country with around 162 million people and the population growth rate was 1.4 per cent. The proportion of urban population was 28 per cent of total population. However, the country belongs to the category of low human development countries ranking 129th out of 169 countries with HDI value 0.463 in 2009 (UNDP 2010). By contrast, with 1,155.3 million people and 1.3 percent population growth, the Indian economy grew at a 9 per cent in 2009. Per capita GDP in India was recorded US$ 1,192 in 2009. The contribution of agriculture to the economy was 18 per cent and its urban population was 30 per cent of total population in the same year. India however, ranks 119th with HDI value 0.512 and belongs to the medium human development countries. In Nepal, GDP growth rate was 4.7 per cent in 2009 and its per capita GDP was US$ 427, the lowest in the region. The contribution of agriculture to the economy was 34 per cent. Population growth rate is comparatively high (1.8 per cent) with 29.3 million
97 population while 18 per cent of its total population lives in the urban areas. Nepal, however ranks 138th among the low human development countries with HDI value 0.423. In Pakistan, GDP growth rate was 3.6 per cent and per capita GDP was US $ 995 in 2009. The contribution of agriculture to the economy was 22 per cent. With 156 million people, Pakistan has the highest population growth rate (2.4 per cent) in the region. Around 37 per cent of its total population lives in urban areas which was the highest in the region. The country however, ranks 125th with HDI value 0.487 and belongs to the medium human development countries. In Sri Lanka, GDP growth rate was 3.5 per cent in 2009 while per capita GDP was US$ 2068, the highest in the region. Population growth rate was 0.7 per cent and 15 per cent of total population lives in urban areas. The contribution of agriculture to the economy was 13 per cent. Sri Lanka maintained a good economic performance with a medium growth of GDP. The country ranks 91th in HDI with value 0.653 and belongs to the medium human development countries. Within the economic profiles of South Asian countries, their key education indicators are presented in Table 3.12. Table 3.12: Key Education Indicators of South Asian Countries (Bangladesh, India, Nepal, Pakistan and Sri Lanka), 2009 Countries/Indicators Sex Bangladesh India Nepal Pakistan Sri Lanka Gross Enrolment rate: Both 95 117*** 126 85 97 Primary level (%) Gross Enrolment rate: Both 42*** 60*** 43 30 87 Secondary level (%) (2006) Gross Enrolment rate: Both 8 13 6 5 NA Tertiary level (%) Mean Years of Schooling Both 4.6 4.30 3.14 4.80 8.14 Completion rate: primary Male 58 95*** 79.6* 68 97 level (%) Female 63 94*** 72.3* 54 98 Literacy rate (15 -24 years) Male 74 88 (2006) 87 79*** 97*** (%) Female 77 74 (2006) 77 61*** 99*** Gender Parity Index14 (GPI) - 1.1*** 0.97** 1.0* 0.84 1.0 Primary level Source: World Development Indicator 2011. An „*” indicates 2005 data, ** indicates 2007 data and „***” indicates 2008 data. NA indicates not available. Note: Mean years of schooling are taken from HDR 2010, UNDP.
14 Gender Parity Index (GPI) refers to the ratio of the female to male gross enrolment ratios. A GPI of 1 indicates parity between male and female.
98 Table 3.12 shows that Bangladesh has achieved higher level of enrolment both at primary and secondary level for boys and girls. It achieved a moderate score in mean years of schooling (4.6 years). By contrast, it performed poorly in primary school completion rate (58 per cent for males and 63 per cent for females) as well as in literacy rate (15-24 years). In these two indicators Bangladesh lagged behind in the region compared to other countries. However, in gender parity index (GPI) at primary level, Bangladesh achieved the score 1.1, highest in the region. This indicates gender parity has already been achieved in this indicator and in some cases girls‟ enrolment exceeded boys‟ enrolment.
India performed well in completion rates at primary level (95 percent for boys and 94 percent for girls) as well as in literacy rates (88 percent for male and 74 percent for female). It scored moderately in mean years of schooling (4.30 percent). The male-female difference in India is not very prominent at primary level as it is indicated by gender parity index (value 0.97). Gender gap is evident in youth literacy rate for young population (15-24 years), as the rates were 88 per cent for males and 74 per cent for females. In the case of Nepal, the completion rate of primary cycle is quite impressive. Although, gender difference persists in youth literacy rates, but gender balance is maintained at primary level as the value of GPI is 1.0.
Pakistan performed well in completion rates at primary school cycle. There is a wide variation in youth literacy rate (15-24 years), as the proportion of literate males was 79 per cent and it was 61 per cent for female. The value of the GPI in Pakistan is 0.8 which indicates the existence of gender disparity in primary level.
In the South Asia region, Sri Lanka scored highly in educational attainment in all respects: enrolments at all levels of education, youth literacy rate, primary school completion rate, mean years of schooling as well as in gender parity index (GPI).
In general, most countries performed well at primary education level while at secondary and tertiary level, enrolment fell drastically in all countries except Sri Lanka. This outcome was due to inadequate resources, facilities and deficiencies in their education policies. Major primary school level performance indicators are shown in Table 3.13.
99
Table 3.13: Key Indicator in Primary School: 2009 Indicator/ Countries Bangladesh India Nepal Pakistan Sri Lanka Completion rate of primary 61 95*** 76* 61 97 school Drop out rate** (%) 45.2 34.2 38.4 30.3 6.6
Repetition rate (%) 12 3** 21 3 1
Pupil-teacher ratio 45*** 40* 33 40 23***
Public Expenditure on 2.4*** 3.1 4.6 2.7 NA Education (as % of GDP) (2006) Source: Country Profiles, World Bank, 2011. An „*” indicates 2005 data, ** indicates 2007 data, and „***” indicates 2008 data. NA indicates data not available.
Table 3.13 shows that the primary school completion rate was 61 per cent in Bangladesh in 2009, which is the second lowest among the region. Around 35-40 per cent of students enrolled at primary level dropped out before completing the 5-year primary school cycle. At primary level, about 12 per cent students in Bangladesh repeated grades (from grade1- grade5). The pupil-teacher ratio was 45:1, which is the highest in the region. These are concerns in educational attainment in Bangladesh. In India, the drop out rate at primary level was also high (34.2 per cent), while in Nepal, the rate was 38.4 per cent and in Pakistan, it was 30 per cent. The repetition rate in Nepal (21 per cent) was the highest in the region. The pupil-teacher ratio was 40:1 same in India and in Pakistan however, both are below from the corresponding ratio of Bangladesh (45:1). These countries on average spent 3 per cent on education as percentage of GDP which exceeds Bangladesh‟s annual education expenditure (2.4 per cent of GDP). In this region, Sri Lanka indeed, performed very well in most education indicators.
3.7 Concluding Remarks This chapter descried the education system in Bangladesh which has three streams of education such as general education, Madrasa education and technical and vocational education with wide diversification in curriculum particularly in primary level of education. It has been observed that in order to achieve overall improvement, successive governments in Bangladesh undertook substantial education policies and programs during the 1990s, for example, introduction of Compulsory Primary Education Act 1990, free education up to grade XII for girls in the rural areas, introduction of Female
100 Secondary School Stipend Program. These measures have resulted in impressive gains in school enrolments. Particularly, girls‟ enrolment has increased considerably and inequality between boys and girls has been removed at both primary and secondary school.
Despite these achievements, it appeared that Bangladesh faces serious obstacles towards the long term success in its education system particularly quality of education. A large proportion of children (around 35-40 per cent) do not complete five year cycle of primary education. At the secondary level, the drop out rates become higher and at the tertiary level, girls‟ participation is around one fourth of the total students. Madrasas and many schools (primary and secondary level) particularly in the rural areas often lack minimum standards to prepare their students for the paid labour market. There are serious shortages of trained teachers, lack of management capability of the heads of the institutions and effective functioning of the school activities. Although the government expenditure on education in Bangladesh is increasing over the years, still it is the lowest in South Asia, 2.4 per cent of GDP compared to 3.5 per cent of regional average. Bangladesh attained moderate level of achievements in the field of education among its regional context. However, despite utmost priority being given to education particularly girls‟ education, half of its population still remains illiterate. Gender disparities in income, employment reflect the poor outcomes of the past investments in education as girls are not benefited proportionately as expected.
From the analysis, it can be conjectured that due to high drop out at primary and secondary level, achievements in the literacy rate are below than the expected rate. The reasons responsible in this respect were identified as low household income, parent‟s low level of education, high expenditure for education, shortage of class room, shortage of trained teacher, lack of infrastructure facilities particularly distance to school, supply of electricity, laboratory facilities. Thus, Bangladesh faces a real challenge in enhancing the literacy rate for its entire population. Consequently, if the trend continues, the country will lose the potential productivity of the future workforce. Thus, it can utilize its potential productivity of the future workforce through higher investment in girls‟ education by rearing fewer healthy and educated children and thus make significant contribution to the economic development.
101 However, although the government policy is providing the context to allow girls to achieve their educational potential, the actual situation is that they fall short of these expectations. This is because their family structure often does not support them to continue their education. The hypothesis in this thesis is that if mothers are better educated, this will result in better educational attainment by their children particularly daughters. This is to investigate through how mother‟s education motivate children‟s school attendance and also look for gender influence i.e. father‟s education on boy‟s school attendance and mother‟s education on girl‟s education. The use of the measure of school attendance in investigating the research questions developed on the basis of literature review are relevant to the context of Bangladesh in relation to women‟s education as described in this chapter. In the next chapter (Chapter 4), an overview of health system in Bangladesh, policies and strategies undertaken by successive governments, achievements to date in various health indicator, constraints and challenges faced by the system, overall women‟s health status and comparative analysis of major health indicators of Bangladesh with other South Asian countries will be presented. The chapter will provide the health and nutrition status of Bangladesh in formulating the basis for empirical investigation for women‟s education which can provide the policies appropriate for the country to invest more on women‟s education in order to improve the nutritional status of the children- the potential workforce.
102
Chapter 4
Health Status of Women in Bangladesh
Chapter 3 provided a review of the education system in Bangladesh. The chapter discussed education policies and strategies undertaken by successive governments. It was found that despite substantial investment in education particularly for girls, which has resulted higher enrolment for girls in achieving similar outcomes to boys at primary level. Gender balance has also been achieved at secondary school enrolment but more girls are dropped out as years of schooling increases. Female participation at tertiary level is one fourth of total participation which indicating the existence of significant disparities at higher levels of education. However, due to appropriate policy support and higher investment in girls‟ education, although, female participation in the labour market has increased, their overall status in terms of wage earnings, low paid employment, poverty level compared to male counterpart is remarkably low. Analysis of education system and socio-cultural perspective of Bangladesh shows that low level of women‟s education restraint their own as well as potential of future workforce since educated mothers keen for higher education for their children by influencing decision at household level. It is thus imperative to exploit further human capital by investing more on women‟s education to contribute in the economy indirectly through playing role at household level.
The objective of this chapter is to provide a similar review of the health system in Bangladesh. The chapter discusses health policies and strategies followed by successive governments and achievements to date in various health indicators such as infant and child mortality, maternal mortality, fertility reduction and population growth. It illustrates constraints and challenges confronted by the health system. It provides an extended analysis of women‟s overall health status particularly in maternal and reproductive health
103 and nutritional aspect. Similar to the discussion in chapter 3, this chapter also compares the important health indicators of Bangladesh to regional countries: India, Nepal, Pakistan and Sri Lanka.
The organisation of this chapter is as follows. Section one presents the health system in Bangladesh while section two describes health policies and strategies undertaken in order to develop the health system. Section three illustrates achievements to date in various health indicators while section four describes the constraints and challenges faced by the health system. Section five presents the overall women‟s status in health sector. Section six compares health indicators of Bangladesh to other South Asian countries such as India, Nepal, Pakistan and Sri Lanka. Section seven concludes the chapter. 4.1 Health System in Bangladesh
Health services in Bangladesh are largely dominated by the public sector. The public health system comprises four tiers viz: i) Union Health sub-centre (USC) or Health and Family Welfare Centre (UHFWC), ii) Thana (sub-district) Health Centre, iii) District Hospital (DHs); iv) Medical College Hospital (MCH) and Post Graduate Institute and Hospital. The health sector covers a wide range of issues related to health, nutrition, population and family welfare and includes the drug sector. However, health services provided to the people are mostly categorised as: primary health care, maternal and reproductive health services, adolescent health services, communicable diseases and non- communicable diseases (GoB 2005b). Primary health care services are provided through union15 and thana health centres. Secondary level health care facilities are provided through district hospitals while tertiary health care occurs through medical college hospitals, post graduate institutes and specialised hospitals at divisional and national levels. The current health infrastructure and facilities in Bangladesh are discussed in the following section.
15 Union is the lowest administrative unit in Bangladesh. The country is divided into 6 administrative divisions, 64 districts, 507 thanas and 4484 unions (BBS 2006). At the same time, union council works as an important unit of local government which is run by the elected members (male: 9 female: 3) and headed by Chairmen. It also works as a centre of all activities at local level.
104 4.1.1 Health Infrastructure and Services
Governments of Bangladesh have implemented several policies and programs to ensure health services for all citizens since its inception in 1971. The explicit goal was to build one union sub-centres, (USC) or union health and family welfare centre (UHFWC) in every union, one health complex in every thana (sub-district), and one General Hospital in every district. The government‟s policy objective in the health sector was therefore to provide a minimum level of health care services to all, primarily through the construction of health facilities particularly in the rural areas. In the 1980s, government efforts towards infrastructure development included the wide spread construction of rural hospitals and dispensaries (WHO 2008). During the 1990s, government initiatives along with private sector and the NGOs‟ activities, include health services such as the Expanded Program for Immunisation (EPI), family planning, nutrition, health education regarding using safe drinking water, sanitation and awareness rising for HIV/AIDS and have extended these throughout the country now covering most rural areas. Current health infrastructure and services are presented in detail as follows.
I. Primary Health Care Services
Primary health care services in Bangladesh are provided by the union level (lowest administrative unit) and thana (sub-district) level health centres. A union level health sub-centre (USC) or family welfare centre (UHFWC) provides out-patient services by field-level staff. The field-level personnel comprise Health Assistants (HAs) in each union, who supposedly make home visits for preventive health care services, and Family Welfare Assistants (FWAs) who supply contraceptives at household level for population control. The USCs/UHWFCs are linked with thana health complexes to provide out- patient services (WHO 2008). A thana level health complex is typically a thirty-one to fifty bedded hospital for in-patient and out-patient services and also has a home-service unit staffed with field workers. In all thana health complexes, nine qualified doctors including four specialists provide health care services to the local population including a limited specialised service, emergency obstetric care and primary treatment for all diseases. Besides these, 2,700 field staffs provide curative services to control diarrhoea, malaria, filaria, goitre and distribution of „vitamin A‟ capsules for prevention of blindness. These thana level
105 health centres work through the union health sub-centres or family welfare centres (WHO 2008). Thirty one-bed thana (sub-district) health complexes were built in all rural thanas (397) and these are now being upgraded to fifty-bed hospitals in phases. In 22 new administrative thanas, a further, thirty one-bed hospital has been established. Thus, primary health care facilities have been expanded throughout the country (GoB 2009a).
II. Secondary Health Care Services
The second layer health facilities for providing health services in Bangladesh constitutes sixty one district16 hospitals (DHs) covering the whole country (GoB 2009a). These hospitals have larger facilities in comparison to the thana health complex, with an average bed size of 133 ranging from 50 to 375 beds. All fifty-bed hospitals at district level were upgraded to 100 beds. Upgrading of some 100-150 bed hospitals to 250 beds is in progress. There were seventeen 50-bed DHs, one 75-bed DH, thirty-three 100-bed DHs, one 120-bed DH, and two 250-bed DHs in 2007 (HEU 2007). District health facilities are more regularly utilized due to the availability of qualified doctors and health services compared to union health centres and thana health complexes (WHO 2008).
At district level, there are Maternal and Child Welfare Centres (MCWCs) which offer emergency obstetric care and clinical contraception, run by the Directorate General of Family Planning. There are ninety eight MCWCs in the country, including sixty two located in each district level17, twelve at thana level and twenty four at union level. Steps are being undertaken to upgrade all the MCWCs from ten-bed hospitals to twenty-bed hospitals in phases (HEU 2007).
III. Tertiary Health Care Services The third layer of the health system includes medical college hospitals, post graduate institutes and specialised hospitals at divisional and national levels (WHO 2008). There are fifteen government owned medical college hospitals (MCHs) with around 650 beds in each hospital in the country for providing tertiary health cares. There are twenty one specialised hospitals for mental, leprosy, infectious diseases, chest
16 Out of total 64 districts in Bangladesh, 3 districts do not have hospital at district level due to having hospitals at divisional level. 17 The construction of the other two MCWCs at the district level is yet to be decided.
106 diseases etc. including the 200-bed Institute of Child and Mother Health (ICMH) and the 600-bed Institute of Diseases of Chest and Hospital (IDCH) in Dhaka City to provide services to these respective areas (HEU 2007). Moreover, privatisation of medical care at tertiary level is expanding and thirty four private medical college hospitals are working in the country. As it is difficult to provide services to such a large population as in Bangladesh, private sector and NGOs are encouraged to work in the health sector. Indicators of major health facilities available in Bangladesh are given in Table 4.1.
Table 4.1: Major Indicators of Health Facilities in Bangladesh, 2009 Facilities Quantity Number of qualified physician 51993 Physicians per 10, 000 population 3 Population per physician 2773 Number of registered nurses 24151 Population per nurse 6180 Physician to nurse ratio 2:1 Number of hospital beds 41107 Hospital beds per 10,000 population 4 Total expenditure on Health as % of GDP 3.4 Public expenditure on health as % of total 32 expenditure on health Source: Government of Bangladesh (GoB), Bangladesh Economic Review, 2010, Ministry of Finance, Bangladesh Director General of Health Service (DGHS), Ministry of Health and Family Welfare, Bangladesh. UNICEF Country Profile.
Table 4.1 shows that hospital beds available per 10,000 population was 4 in Bangladesh while physicians per 10,000 population was 3 in 2009. This indicates health facilities available in the country are really inadequate to meet the medical requirements of the entire population.
The density of qualified health care providers including doctors, dentists and nurses at the national level is 7.7 per 10,000 people which is lower than the WHO threshold level of density for the doctors, dentist and nurses of 22.8 per 10,000 population (GoB 2009b). The average consultation time per patient was 54 seconds shown by Guyon et al. 1994, which is extremely low to correctly diagnose diseases. The scarcity of skilled health personnel and their concentration in major cities/towns are major challenges in the health sector of Bangladesh. It is difficult to provide adequate
107 services to the people when three physicians and less than two nurses are available for per 10,000 population (BHW 2007).
4.2 Health Policies and Strategies The constitution of the People‟s Republic of Bangladesh states that „Health is a basic right of every citizen of the Republic‟ as it is fundamental for development (GoB 2008b). In order to meet this constitutional obligation, successive governments of Bangladesh have pursued policies and strategies that ensure provision of basic health services to the entire population particularly for the vulnerable groups. The health sector policies also emphasised primary health care as the key approach for improving the health status of the people. Bangladesh has endorsed international obligations in respect to health issues. It ratified the Millennium Development Goals (MDGs) at the UN Millennium Summit 2000 and is committed to attain health related MDGs (shown in Appendix Table A3.3). The government of Bangladesh adopted the Health and Population Sector Program (HPSP) in 1998 for the period of 1998-2003. The second phase of the program was for the period 2003-2011 and renamed as Health, Nutrition and Population Sector Program (HNPSP). HNPSP set goals and targets in line with MDGs delineated in the National Strategy for Accelerated Poverty Reduction (NSAPR-I) 2005 (shown in Appendix Table A3.1). The third phase of health sector program is expected to start from July 2011 for the period of 2011-2016. In the case of population, the government has formulated the National Population Policy, 2004 in response to the International Conference on Population and Development (ICPD) 1994 in order to curb the unrestrained population growth. Health related policies, strategies and programs are explained in the following sections.
4.2.1 Health Policies and Strategies In order to improve overall health status, a number of policies and strategies were included in the National Strategies for Accelerated Poverty Reduction (NSAPR I, II) (GoB 2005b; GOB 2008c). Important policies are as follows:
Ensure universal accessibility and equity in health care particularly for the rural population; Increase the coverage of the Expanded Program for Immunisation (EPI);
108 Strengthen Integrated Management of Childhood Illness (IMCI); Give priority to Mother and Child Health (MCH); Strengthen essential delivery services;
The government of Bangladesh also emphasised maternal and reproductive health as a top priority. Major policies in this respect are as follows:
Increase coverage of ante-natal and post-natal care; Expand Emergency Obstetric Care (EOC) services; Increase the number of skilled birth attendees (SBA); Increase contraceptive coverage with method-mix; Encourage male participation in contraceptive use; Enforce the minimum legal age at marriage, Enforce birth registration; Ensure appropriate age for girls for giving first birth; as well as encourage adequate spacing between children; Introduce voucher system for poor pregnant women for having health services. Consistent with national policies and strategies to reduce maternal mortality, various programs in this area are being implemented by the government. NGOs and community- based organizations are also collaborating in implementing these programs. Important programs are: i) provide counselling services aimed at rising the age at marriage; ii) provide adolescents with reproductive health education, iii) ensure confidentiality and security along with safe delivery services; iv) train doctors, nurses, mid-wifes and other paramedics to be women friendly; v) counselling parents, teachers and service providers to handle appropriately adolescent‟s sexual and reproductive health issues, and vi) expand services for hard-to-reach areas (e.g. coastal, hilly, haor and water body, locked by land and emerged new lands at rivers through satellite clinics.
It is worth noting that adolescents constitute more than one-fifth of the total population in Bangladesh and one-fifth of the total births occur among these adolescent mothers. The rates of maternal and infant deaths are comparatively high among these
109 mothers. In addition to the other programs, an Essential Services Package for Adolescent Health Care covering all adolescents was introduced in 2003 (GoB 2005b; 2008c).
Further, infectious and communicable diseases take a heavy toll on lives every year in Bangladesh. Steps taken to restrain communicable diseases are: i) increase detection of smear positive TB; ii) decrease the leprosy prevalence rate; iii) reduce malaria specific mortality; and iv) reduce spread of HIV/STD infection so that it does not exceed the five percent of the risk population. Measures considered in this area are epidemiological and entomological surveillance, strengthening and expansion of the blood safety program, scaling up the Hepatitis B vaccination program, and strengthening the disease surveillance programs. Raising awareness against tobacco and alcohol consumption, regular check-up for early detection of diabetics, cancer and cardiovascular diseases, and strengthening pathological and hospital services are also emphasized (GoB 2005b; GoB 2008c). Malnutrition in Bangladesh is recognised as one of the major problems as it seriously affects the future productivity of workforce. Major policies undertaken in order to address the high prevalence of child malnutrition are: i) reduce protein-energy malnutrition (PEM) for children under two years; ii) reduce the incidence of low birth weight (LBW); iii) reduce the prevalence of anaemia in pregnant women; and iv) reduce the prevalence of iodine deficiency. Consistent with these policies, the National Nutritional Program (NNP) and the Area Based Community Nutrition (ABCN) programs are being implemented to provide nutritional supplements to pregnant and lactating women. Under the „School Feeding Program‟, nutritional supplements are provided to school students. The Government has the policy to work on managing emergency food crises during periods of flood or cyclone and to maintaining stable rice prices particularly for the poor and vulnerable groups. These programs are described in the following section.
4.2.2 Health Sector Programs A sector-wide program for improving maternal and child health and nutrition has been implemented since 1998. Under this program, a package of essential services was provided based on the needs of clients and to deliver services from one point, rather than providing door to door visits by community service workers. This was a major shift in
110 policies which required complete reorganisation of the existing service structure. Moreover, in this health sector program emphasis was placed explicitly on nutritional aspects particularly for poor mothers and children to improve the prevalence of malnutrition. Other specific targets to be achieved by 2015 are: i) reduce total fertility rate from 3.0 to 2.2 per women; ii) increase contraceptive prevalence rate (CPR) from 58 to 72 percent; iii) reduce maternal mortality rate from 3.2 to 2.4 per thousand live births; iv) reduce infant mortality rate from 65 to 37 per thousand live births; v) reduce under five mortality rate (U5MR) from 88 to 52 per thousand live births; and vi) to achieve net a replacement rate18 (NRR) (GoB 2007). In order to achieve these targets, the government has taken a number of steps namely re-introduction of the system of health service delivery through inspection by the family welfare visitors, preservation of unit- wise records in the registrar of the family welfare assistant; reduction of drop out at primary school by follow-up visits, switching to long term birth control arrangements, and bringing the low performing and slum areas under the coverage of a service delivery system. Some large scale programs running in the health sector are described below:
I. National Nutrition Program (NNP): The national nutrition program is being implemented for the period of 2003-2011. The main objective of the NNP is to improve nutritional status of the vulnerable groups, especially women and children through a) behaviour change communication, b) birth weight recording and registration, and c) food and micro nutrient supplements to the malnourished children, pregnant and lactating women. NNP focuses on area-based community nutrition intervention and covers about 28.6 million people (GoB 2008b).
II. Area Based Community Nutrition (ABCN) Program: The core components of this program include children services, maternal (pregnant and post-partum) nutrition services, nutrition services for newly married couples and adolescent girls‟ nutrition particularly for those aged 13 to 19 years. In addition, household food security interventions such as the introduction of nutrition gardens, rearing poultry for nutrition supplements, initiatives for behaviour change communication (contact with patient),
18 Net replacement rate is the rate which indicates a mother will leave behind a girl child, which will be replaced by her.
111 change in behaviour regarding eating, feeding and other caring practices at household level are included in ABCN program (GoB 2009a).
III. School Feeding Program: Under the „School Feeding Program‟, 75 gram fortified biscuits are supplied to students at primary school in order to increase school attendance as well as maintain nutritional requirements.
Moreover a „National Food Policy‟ was launched in 2009 by the Ministry of Food and Disaster Management with a primary focus on food security and food based approaches to nutritional improvement. In order to increase household food security, targeted safety net programs such as integrated food security (IFS), vulnerable group development (VGD), food for work (FFW) and „voucher scheme for pregnant women‟ of the poor have been implemented by successive governments. The Public Food Distribution System (PFDS) provides a first line defence in the event of a food emergency. In the period of food emergency, the vulnerable groups also benefit from this public food distribution system.
4.2.3 Drug Policy The drug sector is an important and integral part of health services in Bangladesh, because around 40 percent of total patients (BBS 2006d) not only buy medicine from pharmacy/dispensary but also receive treatments from medicine sellers. Low quality drugs and selling drugs without a doctor‟s recommendation are widely practised particularly in the rural areas and are frequently the source of serious health hazards. Drug production, quality of drugs, storage, and distribution through private and public channels needs to be strictly regulated by the government (GoB 2005b). The first National Drug Policy (NDP) was formulated in 1982 and a new National Drug Policy 2005 was approved by the government. The main objective of the drug policy is to ensure an adequate supply of safe, effective and useful drugs at affordable prices. Currently the activities of the drug sector are regulated by the Drug Act 1940 and the Drugs (Control) Ordinance 1982. The Drug administration is being upgraded to the Directorate of Drug Administration and needs to be further developed as an effective regulatory body in the drug sector (GoB 2005b).
112 In Bangladesh, a significant proportion (6.1 per cent) of patients depends upon alternative medical treatments (BBS 2006d). These types of medical care include traditional ways of treatment, homeopathy, Ayurvedic and Unani (herbal medicine) treatments. The majority of alternative treatment providers do not have any formal education in their system of treatments and in using medicine. These types of treatments and the respective medicines used are not regulated by the government and often clients fall victim to „quacks‟. Nevertheless, due to it being less expensive compared to modern treatment, a large number of poor people still seek these types of medical care. It is therefore considered essential to take steps to improve the standard of alternative treatments through quality control and appropriate training of providers. In order to improve alternative medical care providers, a registration and licensing system has been introduced. Establishment of dependable alternative medical care may reduce the expenses of medical treatment and also reduce pressure on the formal health care system. The new drug policy has attempted to address safety, quality and affordability issues related to alternative medical services (GoB 2005b).
4.2.4 Population Policy The Government of Bangladesh affirmed a population policy in its First Five-year Plan (1973-1978) which emphasised rigorous steps to slow down population growth. The family planning program received virtually unanimous, high level political support. Further, all subsequent governments have identified population control as a top priority for government action. In 1976, rapid population growth was declared to be the country‟s number one problem and a broad-based, multi-sectoral family planning program was adopted along with a population policy. Population policy was seen as an integral part of the total development process and was incorporated into successive five-year plans. Policy guidelines and strategies for the population program are generally formulated by the National Population Council (NPC) which is chaired by the Prime Minister (NIPORT 2005). The government instituted the deployment of full-time local Family Welfare Assistants (FWA) as community based family planning motivators and distributors in the mid-1970s. A social marketing program to promote the sale of birth control pills and condoms was also initiated in that period (NIPORT 2005). With the homogeneous
113 lineage system and cultural heritage in Bangladesh, although being a Muslim country, the population movement was largely successful in encouraging the widespread use of contraceptives and thereby population growth was reduced. This was also recognized in the international arena within a decade (Muhuri 1995). Population programs have been functionally integrated with health programs from the administrative point of view since 1980s under the same Ministry, Ministry of Health and Family Welfare. Therefore, the health and population sector program (HPSP) 1998-2003 was adopted in 1998. This program latter included nutritional issues and was renamed as the health, nutrition and population sector program (HNPSP) for the period of 2003-2011. This program is expected to continue for the period of 2011-2016. Thus, population policy and programs in Bangladesh have evolved through a series of development phases within the health system. 4.2.5 Financial Allocation to Health Sector The budget allocation for the public health sector has been steadily increasing over the last decade in Bangladesh. Annual budget allocations to the health sector are shown in Table 4.2.
Table 4.2: Government Budget on Health Sector during 1990-91 to 2007-08
Fiscal Year (FY) Budget (Taka in million) Percentage change (%)
1990-91 8630 - 1995-96 16110 86.67 2000-01 26270 63.06 2001-02 26490 0.83 2002-03 27970 5.59 2003-04 34450 23.2 2004-05 31750 -7.84 2005-06 41120 29.50 2006-07 49570 12.05 2007-08 52610 1.06 Source: Bangladesh Economic Review 2004. 2007, 2010, Finance Division, Ministry of Finance and Bangladesh Planning Commission.
Table 4.2 shows that the annual budget allocation to the health sector increased nearly 87 per cent from FY1990-91 to FY1995-96. During the period from 1995-96 to 2000-01, the budget allocation increased about 63 per cent but it abruptly dropped to an increase of
114 only 0.83 per cent in FY2001-02. In FY2002-03, the allocation increased to 5.59 per cent from the previous year (FY2001-02) and it again increased 23 per cent in 2003-04. The allocation dropped (-7.84 per cent) in 2004-05 while it increased 29.5 per cent to the following year (2005-06). In 2006-07, the budget allocation increased 12 per cent and it increased only one percent in the following year 2007-08. Thus the health sector budget allocation has not shown a regular trend over the past years. This behaviour however, indicates policy changes occurred in health sector and thus creating sluggishness in achieving policy goals particularly reducing maternal mortality and child malnutrition Explain how this has been a problem in terms of achieving policy goals.
As a result of a policy change towards sector-wide program and thus the budget allocations to health sector, major indicators such as total fertility rate, population growth rate, child mortality rate improved, although, worryingly, the tendency of a child being stunted and underweight virtually remains stagnant during the last few years. This also reflects that the health sector is yet to efficient in performing various programs as it is expected. Achievements to date in various health indicators are described in the following section.
4.3 Achievements in Health Issues
The governments of Bangladesh have implemented various policies and programs for a healthy and capable population with a view to involving them in mainstream development activities (GoB 2007). In addition to the government initiatives, a number of NGOs and private organisations are providing clinic facilities, services on immunisation, mother and child care (MCH), family planning, nutrition, health education on using safe drinking water, sanitation, control of epidemics, endemic diseases and supply of essential drugs and awareness rising HIV/AIDS. As a result, considerable progress has been made in Bangladesh in the field of immunisation, reduction in infant and child mortality, maternal mortality, prevention of communicable diseases and improvement of life expectancy. Achievement in reducing of total fertility rate (TFR) and thus the reduction in population growth rate is also satisfactory. Achievements in infant and child mortality and maternal mortality are explained below.
115
4.3.1 Child Mortality Rate
Child health in general improved in Bangladesh during the 1990s. The infant (0-1 year) mortality rate (IMR) fell from 94 per thousand live births in 1990 to 41 in 2008 (BBS SVRS 2008). The average annual reduction between 1990 and 2008 was more than three per cent. If this trend continues, Bangladesh will meet the MDG target of reducing infant mortality to 31 per thousand live births by 2015 (GoB 2007). This is shown in Figure 4.1.
Figure 4.1: Trend of Infant (0-1 year) Mortality Rate during 1990-2008
Trend of Infant Mortality 100 90 80 70 60 50 40 30 20
10 No. of Infant (per '000'(perbirths) live Infant of No. 0 1985 1990 1995 2000 2005 2010 Year
Source: Bangladesh Economic Review, 2007, Ministry of Finance, Government of Bangladesh.
Figure 4.1 shows that significant success has been made in reducing infant mortality. This success is mostly due to the government‟s Expanded Program for Immunisation (EPI) which has been able to record considerable success in combating infant and child mortality and morbidity. For example, the proportion of one year old children immunised against measles was 54 per cent in 1991, which increased to 82 per cent in 2009 (GoB 2010b).
116 The success in infant mortality positively impacts on child mortality which is evident in the reduction in child mortality. The trend in the child (1-4 years) mortality rate is shown in Figure 4.2.
Figure 4.2: Trend of Child Mortality Rate (1-4 year) during 1990-2008
Trend of Child Mortality 6 5 4 3
live birth 2
1 No. of per Child '000' 0 1985 1990 1995 2000 2005 2010 Year
Source: Bangladesh Economic Review, 2007, Ministry of Finance, Government of Bangladesh.
Figure 4.2 shows that the child mortality rate declined from 5 per thousand live births in 1990 to 3.1 in 2008 indicating a steady improvement in child mortality. The child mortality rate shows a slight upward trend from 2001 to 2002, however the rate again fell after 2005. The mortality rate was 3.1 per thousand live births in 2008. However, child mortality in Bangladesh is still high compared to other developing countries.
4.3.2 Maternal Mortality Ratio In Bangladesh, the maternal mortality ratio (MMR) has improved steadily. The MMR reduced from 574 per 100,000 live births in 1990 to 340 in 2008 (GoB 2009a). According to MDG, the ratio needs to reduce by three quarters from 574 per 100,000 live births in 1990 to 144 by 2015, if it has to meet the target set in this regard. The trend in maternal mortality ratio is shown in Figure 4.3.
117 Figure 4.3: Trend of Maternal Mortality Ratio during 1990-2008
Source: Bangladesh Economic Review 2007, Ministry of Finance, Government of Bangladesh.
Figure 4.3 showed that maternal mortality ratio declined sharply from 1990 to 2000 while the ratio moved upward slightly from 2000 onward. The maternal mortality ratio is again declining and it was 340 in 2008, which is still high compared to the rest of this region. If this trend continues it would be difficult to attain its MDG target of achieving 144 maternal mortality ratio per 100,000 live births by 2015.
4.3.3 Nutritional Status of Children Child nutrition has improved in Bangladesh over the last decades. The rate of stunting (reduced linear growth of height or length compared to the expected growth in a child of same age) declined from 68.7 percent in 1985/86 to 49 percent in 1999/00 and similarly, the rate of underweight (a deficit in body weight compared to the expected weight of a child for the same age) declined from 72 percent to 51 percent for the same period (BBS 2007b).
The nutrition related surveys19, the Child Nutrition Survey 2000, Bangladesh Bureau of Statistics (BBS 2002b) and the Bangladesh Demographic and Health Survey 1999-2000, National Institute of Population Research and Training (NIPORT 2002)
19 The Child Nutrition Survey, 2000 was conducted by Bangladesh Bureau of Statistics (BBS) for the reference period 2000 and the Bangladesh Demographic and Health Survey 1999-2000 was conducted by National Institute of Population, Training and Research (NIPORT) under the Ministry of Health and Family Welfare.
118 Bangladesh, all indicated that nearly one-half of children below the age of six years were moderately underweight or stunted. In Bangladesh, on average 10-18 percent of children were severely underweight or stunted in the sense of being more than three standard deviations below the relevant National Centre for Health Statistics (NCHS) USA standards20 (World Bank 2005). According to the Child and Mothers Nutrition Survey (CMNS) 2005, 46.2 per cent of children under five years of age were moderately stunted while 39.7 per cent of children was moderately underweight (BBS 2007b). The average quantity of food items consumed was estimated at 893 grams per person per day in 2000 and it was 947.8 grams in 2005, which indicates around a six percent improvement during this period (BBS 2007a). Despite this improvement in calorie consumption, progress in reducing malnutrition viz. stunting and underweight has slowed markedly since 2000. The level of child malnutrition in Bangladesh is not only the highest in the world, it still remains a formidable challenge for the country.
Lower levels of income are one of the major causes of the failure to reduce malnutrition. Large differences in malnutrition are observed across the income groups (Jahan & Hossain 1998; Haddad et al. 2003) because low income indicates lower purchasing power for buying food which has a direct bearing on nutrition. In Bangladesh, nearly one third of the children from the richest quintile also suffered from malnutrition which suggests that factors other than income play an important role. Such factors include per capita food intake, infant feeding practices, maternal schooling and hygiene practices, access to safe drinking water, sanitation and health facilities, quality of village infrastructure and protection against natural disaster (GoB 2009b). The report of HKI, Bangladesh (1998) indicated that the prices of rice are of particular importance to child nutritional status in Bangladesh, as more than 78 percent of the calories in the average diet come from grains. Palmer-Jones (2005) showed that child nutrition in Bangladesh depends largely on the price of rice as most (around 70 per cent) of the household‟s food expenditure is incurred on purchasing rice. Increases in the prices of rice therefore, significantly increase malnutrition since wages are not usually adjusted
20 CNS-The Child Nutrition Survey 2000 is conducted on the sample population of „Household Income and Expenditure Survey, 2000‟ on those families have at least on child was in the age of 6-71 months. This survey basically follows the National Center for Health Statistics (NCHS), (USA)/WHO, USA standard in calculating height-for-age z-score (HAZ) and weight-for-age z-score (WAZ).
119 with increased prices. Seasonal trends in rice production and its impact on rice prices thus have implications on household expenditure, household food consumption, food security and the nutritional status of population (HKI/Bangladesh 1998). Seasonality is thus of concern in measuring child nutrition particularly in the pre-harvesting period (Palmer- Jones 2005). Nutrition is therefore, the outcome of the complex interaction of a large number of factors related to income, per person calorie consumption, parent‟s education and health care and hygienic practices at home combined with external supply factors (BBS 2002b).
4.3.4 Fertility Reduction and Population growth
The government of Bangladesh has implemented a broad-based, multi-sectoral family planning program along with an official population policy since the mid 1970s. Population policy was seen as an integral part of the total development process and was incorporated into successive five-year plans. As a result, substantial success was achieved in widespread use of contraceptive which contributes to reducing the total fertility rate (TFR). This in turn reduces population growth. TFR reduced from 2.7 per cent in 1990 to 2.3 per cent in 2008 (GoB 2009a). Similarly, the rate of population growth reduced to 1.4 per cent in 2008 from 2.1 per cent in 1990 (GoB 2005b). Although population growth reduced remarkably, due to the existing TFR of 2.3 percent and predominantly young age structure, population will continue to grow rapidly for some time. Population is expected to be stabilized in 2050, but until then population growth is a serious concern for Bangladesh as the country is already over burdened with a large size of population. The major health indicators in Bangladesh are given in Table 4.3.
120 Table 4.3: Trend of Major Health Indicators of Bangladesh during 1990-2008 Indices/year 1990 1995 2000 2005 2006 2007 2008 Life expectancy (at birth) 56 58.7 63.6 65.2 65.4 66.6 66.8
Infant (<1 year) mortality rate/ 94 71 58 50 45 43 41 000‟ live births Children (1-4 years) mortality 5.0 4.6 4.2 4.1 3.9 3.6 3.1 rate per 000‟ live births Maternal mortality rate (per 574 450 320 348* 337 350 340 100,000 live births) Contraception use rate (%) 50.0 48.7 53.6 57 58.3 59.0 52.6
Total fertility rate per women 2.7 3.5 2.6 2.46 2.41 2.39 2.30 (15-49 years) Average age at Male 25.0 27.5 27.7 23.3 23.4 23.6 23.8 marriage (years) Female 19.0 19.9 20.4 17.9 18.1 18.4 19.1
Population growth rate (%) 2.1 1.5 1.5 1.4 1.4 1.4 1.4
Source: Bangladesh Economic Review 2007, 2010, Ministry of Finance, Government of Bangladesh. Note: Year 1990 indicates benchmark data. An „*‟ indicates as per 10th revision of international classification of diseases. Table 4.3 shows that Bangladesh has achieved improvement in life expectancy, reductions in infant and child mortality, maternal mortality and increased use of contraceptives and thus a decline in population growth. The Table shows that average life expectancy increased from 56 years in 1990 to 66.8 years in 2008, which indicates a significant improvement in overall health status in Bangladesh. The infant mortality rate has been reduced remarkably from 94 (per thousand live births) in 1990 to 41 in 2008. In the case of child mortality, a steady decline is observed during the same period. The maternal mortality rate (per 100,000 live births) has improved from 574 in 1990 to 340 in 2008, which is still the highest in the world. Contraceptive use and the corresponding total fertility rate in Bangladesh also improved during the period of 1990-2008. By contrast, the average marriage age for boys reduced from 27.7 years in 2000 to 23.8 years in 2008 while girls‟ age at marriage remained almost the same at 19 years in 2008 as it was in 1990. This is a serious concern for Bangladesh because early marriage and its consequences already impose significant pressure on health services and worsens the status of child nutrition. Issues affecting health, nutrition and population are analysed in the following section.
121 4.4 Constraints and Challenges in Health Sector Considering the large population size (around 162 million) in Bangladesh, facilities created in the health sector are not sufficient to provide the required health services to all citizens. There is only one hospital bed for every 2773 people and one physician for every 2,500 people (Ministry of Health and Family Welfare 2010). The mass of people, particularly in the rural areas, face serious difficulties in receiving proper treatment and health services for medical requirements.
4.4.1 Infant and Child Mortality Rate
Despite commendable success in reducing child mortality during the 1990s, the rate of improvement over the past few years is comparatively slow. The neonatal mortality rate (death in the first months after birth) is a major contributor to the burden of infant mortality (death before first birthday). According to the BDHS 2007, neonatal mortality is higher among younger mothers (<20 years of age) with 55 per 1000 live births, while among mothers of 20-29 years, the rate is 30 and it is 38 among the mothers more than 30 years. Deaths in the neonatal period account for more than two-thirds of all infant deaths. Acute respiratory infection alone causes 25 per cent of deaths in children aged below five years while 13.4 per cent children died due to malnutrition although mothers who had only primary education as shown in appendix Table A7.4 (NIPORT 2005). Among children under-five years, prematurity of the newborn, low birth weight and malnutrition together contribute to about 45 per cent of all deaths (NIPORT 2005). Moreover, socio-economic status is highly correlated with child mortality, a mother‟s level of education and household wealth are inversely related to a child‟s risk of dying (GoB 2009d). In order to achieve the MDG target regarding under-five mortality of 48 per thousand live births by 2015, malnutrition and neonatal mortality need to be addressed through concerted efforts in collaboration with immunisation, awareness raising and other supportive programs.
[4.4.2 Maternal Mortality Ratio Although the maternal mortality rate declined steadily since 1990s, the reproductive21 health status of Bangladeshi women is very poor. Millions of women in
21 Reproductive health implies safe pregnancy and childbirth, free from the fear of unwanted pregnancy and contact of communicable diseases.
122 Bangladesh experience high life threatening risks, chronic or other serious health problems related to pregnancy and childbirth every year (GoB 2009b). Consequently, the maternal mortality rate is 340 per thousand live births in 2008 which is the highest in the world. If the present trend in reducing MMR continues, attainment of a maternal mortality ratio to 144 by 2015 as per MDG target would be difficult.
Lack of available maternal health services at and around the time of birth is one of the contributing factors to high maternal mortality. The proportion of deliveries assisted by skilled personnel (skilled birth attendants) was only 24 per cent at national level (GoB 2010). Traditional birth attendants performed nearly two-thirds of the deliveries at home. Only 50 per cent of pregnant women utilize ante-natal care and 16 percent use post-natal care. Poorer and less educated women are less likely to seek qualified routine or emergency obstetric care (GoB 2005b).
The leading causes of maternal mortality are identified as haemorrhage, abortion, injuries, eclampsia, sepsis and obstructed labour (NIPORT 2008). Maternal malnutrition is also an underlying cause of many deaths because half of the pregnant women suffer from malnutrition and anaemia that contributes to low birth weight babies and neonatal mortality. The health seeking behaviour of women during their pregnancy period and child birth is also poor. A study by the Institute of Public Health and Nutrition (IPHN 2001) reports a high prevalence of anaemia across all vulnerable groups - 46 per cent among the pregnant women, 64.5 per cent among 6-23 months old children and 42 per cent among 24-59 months old children. Iodine deficiency is also a public health concern through-out the country, with about 34 per cent of children 6-12 years old and 39 per cent among the 15-44 years age group suffering from sub clinical Iodine deficiency (HKI/Bangladesh 2001). Due to these factors, the maternal mortality ratio is considerably higher in Bangladesh compared to South Asian Countries.
4.4.3 Nutritional Status of Children The level of child malnutrition in Bangladesh is the highest in the world. Although the country made significant progress in reducing stunting, underweight and wasting during 1990s, progress slowed markedly after 2000. The most alarming trend is that the level of wasting has increased by more than 50 percent during this period from 10
123 percent in 2000 to 16 percent in 2007 which is worrisome for a low income country like Bangladesh (WHO 2008). The prevalence of malnutrition in Bangladesh is shown in Figure 4.4.
Figure 4.4: Prevalence of Malnutrition in Bangladesh
Source: NIPORT 2008, Bangladesh Demography and Health Survey 2007 (the latest available demographic survey).
Figure 4.4 shows that stunting continued to fall but the proportion of children being underweight barely changed, falling 2 percentage points to 46 percent in 2007 from 48 per cent in 2000. The level of wasting has increased by more than 50 percent during this period from 10 per cent in 2000 to 16 per cent in 2007 (NIPORT 2008). The report of BDHS 2007 showed that malnutrition is widening among different groups. Stunting is most common in the poorest households where more than 50 per cent of the children are too short for their age compared to only 26 per cent in the wealthiest households. Stunting is also more prevalent in rural areas (45 per cent) than in the urban areas (36 per cent) and also high among uneducated households compared to those with higher education. Of most concern is that wasting in Bangladesh stands above the emergency threshold of 15 per cent according to the WHO‟s classification. Gender differences in malnutrition are most pronounced at young ages. Girls aged 6-11 months are significantly more likely to be underweight than similarly-aged boys due to inadequate supplementary food and also negligence in providing treatment while girls are sick. Although the
124 situation is improving, difference between boys and girls in terms of stunting and underweight still persist (NIPORT 2008).
4.4.4 Rural-Urban Differences The differences between rural and urban areas in the availability of medical facilities remain wide. These are reflected in various health indicators such as the distribution of qualified health care providers is highly urban biased, being 1.1, 0.8 and 0.8 per 10,000 population in the rural areas as against 18.2 physicians, 5.8 nurses and .8 dentists in the urban areas (GoB 2009b). Moreover, in many cases particularly in the rural areas, facilities remain unutilized due to lack of skilled health personnel, high levels of doctor‟s absenteeism and lack of maintenance of public hospitals. The level of malnutrition among the rural children is higher than their urban counterparts. Stunting is also more prevalent in rural areas (45 per cent) than in the urban areas (36 per cent). Stunting is also high among uneducated households compared to households with higher education (NIPORT 2008). Similarly, underweight is high among the rural children than the urban children. Furthermore, the meagre health services for the rural population in Bangladesh are severely disrupted during the monsoon season or in natural disasters.
4.4.5 Lack of Resources In Bangladesh, implementation of health policies and programs are seriously constrained by financial resources as well as by a lack of skilled health personnel. Financial resource constraints on the health sector are mainly due to higher level of poverty, unwillingness of donors to support infrastructure development and a serious lack of coordination in internal resource mobilisation. In order to reduce the burden, the government has recently emphasised cost sharing and decentralisation of authority, decision making and program implementation at local level institutions. Promotion of community participation and delivery of essential service packages to the poor and mobilisation of financial resources by negotiating with donors such as the World Bank, Asian Development Bank and European Commission are also being considered (GoB 2009a). To overcome the construction and the maintenance problem regarding health facilities, the government is considering the introduction of a more need-based health planning process that will involve all stakeholders and the community. For effective and
125 efficient management of drug procurement and distribution in the public sector, which is highly inefficient and full of pilferage and corruption, a strong administrative system is essential.
Moreover, deficiencies in some key micronutrients have important adverse effects among children in Bangladesh. Low birth weight is also prevalent. Children who are smaller at birth than the average size have a higher risk of growth and development as they grow older. Approximately 50 percent of babies are born with a low birth weight (below 2500gm) in Bangladesh and are more likely to be underweight or stunted (GoB 2005). The World Bank‟s MDG Report (2005) indicates that Bangladesh has far higher levels of child malnutrition than would be expected at its level of per capita GDP. In South Asia, Bangladesh lags behind India and Pakistan in nutritional status (World Bank 2005). Jahan and Hossain (1998) found that economic variables such as household income and expenditure are closely related with child nutrition. Major causes of high level of child malnutrition are sub-optimal infant and young child feeding practices (IYCF), chronic energy deficiency, maternal malnutrition, short birth intervals, lack of safe water, unhygienic sanitation practices at household level and importantly, low level of maternal education (ADB 2004; GoB 2010b). Improvements in child nutrition are therefore dependent on increasing access to health care, food intake, safe water and sanitation, better breast-feeding practices and include women's education.
Nevertheless, within the existing health system, serious problems have been found in service delivery, low quality of services, incorrect diagnosis of diseases, low quality of drugs, abuses of drugs etc. Similarly, from the nutrition point of view, Bangladesh endures a high prevalence of malnutrition as discussed in section 4.3.3 and 4.4.3. This high malnutrition is mainly caused by chronic energy deficiency, protein-energy malnutrition and inappropriate food intake, use of non-safe water and unhygienic sanitation at household level and lack of female education.
As discussed above, stunting and underweight children are the end result of a series of problems within the health services of Bangladesh. A range of policies and programs are being provided to help overcome these problems. However, the uptake of these opportunities has been low, particularly in poorer families in rural locations. In a
126 situation of inadequate universal supply of health services, family related factors can be a major determinant of child health status, and hence their future economic productivity. This thesis attempts to look particularly into the effect of mother‟s education on whether a child is being stunted or underweight by using data on stunting (low height) and underweight (low weight) from the household survey.
4.5 Overall Women’s Status in Health Services
Over the years, the governments of Bangladesh have been implementing various policies and programs in order to improve women's health status. As a result, reduction in fertility rate and the use of contraceptives improved and thus population growth has reduced. But the maternal mortality ratio still high among the region. Children's nutrition status is deteriorating (GoB 2009a). This indicates investment in women‟s health and nutrition is not only insufficient but also health facilities availability in the country is very low. Beyond the health system, socio cultural norms and customs also significantly affect women's health. In a patriarchal society such as found in Bangladesh, women‟s health is compromised largely by their low socio-economic status. Women, particularly poor women, suffer from diseases related to poverty and malnutrition and ignorance of proper hygiene. The real thrust of reproductive health strategies and programs must ensure that women are able to fulfil their reproductive potential safely because the burden of reproductive illness is solely borne by the women themselves. They are also bound to take most of the responsibility for using contraceptive in keeping family size small compared to men. As a result, women face hardship in bearing their reproductive responsibilities and pay a substantial cost in this regard.
Analysis of consumptions pattern show that in the same household, it favours boys rather than girls‟ and thus this causes acute malnutrition for the girls. As they grow older, women suffer from higher levels of mortality and morbidity, mainly because of the fact that they suffer from higher levels of malnourishment (ADB 2004). This impact is aggravated by women‟s reproductive function. Socio-cultural conventions also aggravate the situation as these constrain women from accessing health care. Women need approval from their family members either husband or elderly people before seeking medical care in the case of sickness and often less priority is attached to women‟s general health.
127 Health care facilities often do not provide sufficient privacy for the female patients. Thus, women‟s health problems are largely affected by socio-economic and cultural constraints (GoB 2008c). Along with other programs, improvement in women‟s health requires a change in the socio-cultural dimensions of society. Since most causes of morbidity and mortality affecting women are largely preventable and amendable to simple interventions, improving women‟s access to health services and addressing the underlying socio-cultural factors require more resources to be invested in health related policies and programs (WHO 2008).
4.6 Comparison among South Asian Countries
Despite South Asian countries, particularly India, Pakistan and Sri Lanka experiencing high economic growth during the last decade, this region still has high rates of infant and maternal mortality and also has the largest number of undernourished children in the world (World Bank 2009). Although it is argued that poverty is often the underlying cause of high infant mortality and high level of child under-nutrition, the high economic growth experienced by South Asian countries has made no significant impact in improving these indicators except in Sri Lanka. Indicators regarding population and health facilities among these countries in 2009 are presented in Table 4.5.
Table 4.5: Indicators of Health Facilities in South Asian Countries in 2009 Indicator/ Countries Bangladesh India Nepal Pakistan Sri Lanka Population (million) 162.0 1155.3 29.3 169.7 20.3
Physician per 10,000 people 3 6 2 8 6
Hospital Beds per 10,000 4 9 50 6 31 people Access to improved water*** 80 88 88 90 90 (% of population) Access to improved 53 31 31 45 91 sanitation*** (% of population) Expenditure on Health (as % of 3.4 4.2 5.8 2.6 4.0 GDP) Per capita expenditure on 18 45 25 23 84 health ( current US $) Source: World Development Indicators, 2011, *** indicates data for 2008. Note: data for physician per 10,000 people are taken from Human Development Report 2010.
128 Table 4.5 exhibits that in South Asia, India has the largest population (1155.3 million) while Sri Lanka has the smallest size of 20.3 million. Bangladesh and Pakistan also have comparatively large population with 162.0 million and 169.7 million respectively. The size of population in Nepal is 29.3 million.
Physicians available per 10,000 people was 3 in Bangladesh, 6 in India, 2 in Nepal, 8 in Pakistan and it was 6 in Sri Lanka indicating access in Bangladesh and Nepal was well below that of the other countries. Hospital beds per 10,000 people was 50 in Nepal and 31 in Sri Lanka while it ranged 4 to 6 in Bangladesh, India and Pakistan. In all South Asian countries included in this study, on average, more than eighty percent of the population had access to improved water while in Bangladesh, the rate is just eighty percent. In the case of access to improved sanitation, the coverage ranged from 31 to 53 percent in Bangladesh, India, Nepal and Pakistan while it was significantly higher in Sri Lanka at 91 percent. Total expenditure on health as a percentage of GDP was 3.4 percent in Bangladesh, 4.2 percent in India, 5.8 percent in Nepal, 2.6 percent in Pakistan and 4.0 percent in Sri Lanka. Therefore, per capita expenditure on health was US$ 18 in Bangladesh, US$ 45 in India, US$ 25 in Nepal, US$ 23 in Pakistan and US$ 84 in Sri Lanka. It is worth noting that per capita expenditure on health is almost similar in Bangladesh, Nepal and Pakistan (ranged from US$ 18 to US$ 25) while it was US$ 45 in India and US$ 84 in Sri Lanka.
Thus, among these countries, Sri Lanka performed very well in all indicators of health facilities described in Table 4.5 compared to other countries of South Asia. A similar situation was found in key indicators of education as discussed in Chapter 3. However, among the South Asian countries, Bangladesh holds the worst position regarding hospital beds per 10,000 people and per capita health expenditure.
In order to provide a comparative picture regarding demographic and social development in the South Asia region, key indicators of health status among the countries are presented in Table 4.6.
129
Table 4.6: Health indicators: Bangladesh, India, Nepal, Pakistan and Sri Lanka in 2009 Indicators/Countries Bangladesh India Nepal Pakistan Sri Lanka Life expectancy at birth (year) 67 69 67 67 74 Infant (<1 year) mortality rate (per 41 50 39 71 13 „000‟ live birth) Under 5 Mortality rate (per „000‟ 52 66 48 87 15 live birth) Prevalence of underweight children 45 43.5(2006) 39 (2006) 38* 21.6 under five years of age (%) Maternal mortality rate*** (per 340 230 380 260 39 100,000 live birth) Skilled birth attendant (%) 24 53*** 19 (2006) 39 96* Contraception use rate (%) 53 *** 54 48 (2006) 30** 68 ** Total fertility rate (15-49 year) (%) 2.3 2.7 2.8 3.9 2.3 Population growth rate (%) 1.4 1.3 1.8 2.1 0.7 Source: Human Development Report (HDR), UNDP, 2005. * indicates 2005 data, ** indicates 2007 data and *** indicates 2008 data. Note: Latest available figures are given in the table.
Table 4.6 shows that life expectancy was 74 years in Sri Lanka in 2009 while it ranged from 67 to 69 years in the other four countries. In the case of infant mortality, the rate was 13 per 1000 live births in Sri Lanka while it was significantly higher at 41 in Bangladesh and 50 in India and 39 in Nepal. The rate was 71 in Pakistan, which is highest in the region. Similarly, in the case of children‟s under-five mortality, Pakistan has the highest rate at 87 per 1000 live births while the rate was 66 in India, 52 in Bangladesh and 48 in Nepal. Sri Lanka has the lowest ubder5 mortality rate at 15 per 1000 live births. In the case of underweight children, Bangladesh, India, Nepal and Pakistan have the similar rate of underweight children while Sri Lanka has the lowest (21.6 per 1000 live births). In South Asia, all five countries have a large number of underweight children including Sri Lanka.
In maternal mortality, all countries except Sri Lanka were in vulnerable situations. The maternal mortality ratio in Sri Lanka was only 39 per 100,000 live births. In the case of contraceptive use, the rate was 68 percent in Sri Lanka followed by India (54 per cent) and Bangladesh (53 per cent). The rate was 48 percent in Nepal. In Pakistan, the
130 contraceptive use rate was 30 per cent which was the lowest in the region. The resultant effects were reflected in the countries‟ total fertility rates. The lowest fertility rate was in Sri Lanka (2.3 per cent) as well as in Bangladesh (2.3 per cent) and the highest rate was in Pakistan (3.9 per cent). Population growth rates however, varied in these countries. In Pakistan, population growth rate was 2.1 per cent which was highest in the region and Sri Lanka has the lowest population growth rate of 0.7 percent. In Nepal, the rate was 1.8 percent and 1.3 per cent in India and it was 1.4 percent in Bangladesh.
As analysed the demographic and health indicators of South Asian countries in 2009, it is observed that Sri Lanka performed well in most of key demographic and health indicators e.g. average live expectancy, infant and under five mortality rate, maternal mortality ratio, fertility rate and population growth rate in the region. Sri Lanka has given priority in social sector investment, particularly population, health and nutrition programs in order to improve nutrition and health status of the population as a whole. Importantly, the achievements in social sectors was materialised with low level of per capita income as Sri Lanka holds. Therefore, although, Bangladesh‟s relative position is no the worst in most indicators presented in Table 4.6 but still it needs improvement in most areas particularly the prevalence of underweight of children under 5-years of age.
4.7 Concluding Remark This chapter described the health system in Bangladesh which works through four tiers: union level health centres, thana health complexes, district hospitals and national level medical college hospitals. As was described, construction of infrastructure was emphasized during the 1980s in order to meet the nation‟s medical requirements while immunisation of children and other primary health care services was expanded during the 1990s. However, the health sector in Bangladesh incorporates a wide range of issues from primary health care to tertiary level health services including reproductive health, population and family planning services and nutritional policies and programs. The sector also covers the severe problems of communicable and non- communicable diseases. Although the private sector is also expanding rapidly, most of the responsibility for these issues rest with the government. The major health indicators showed steady gains in the overall health status of Bangladesh since 2000. Service
131 delivery in the public health sector is extremely poor. Particularly, in the rural areas, facilities remain unutilized due to lack of skilled health personnel, high levels of doctor‟s absenteeism and lack of maintenance of public hospitals. The low quality of drugs, procurement of drugs and their distribution are highly inefficient and affected by pilferage and corruption. Further, program implementation has been seriously limited by proper utilisation of finance, health policy changes, inefficient program management and supervision, shortage of skilled health personnel, inadequate staff performance and insufficient supply of medical equipments and thus severe gaps in the supply of most health services still exist. However, there are number of issues in the health sector such as maternal mortality rate and child malnutrition among children and serious lack of awareness among the families particularly mothers to maintain a healthy life for their children are the matters of serious concern. Among South Asian countries, it is appeared that Bangladesh lags behind India and Pakistan particularly in the case of child malnutrition. Successive governments in Bangladesh have implemented various health, reproductive and maternal health and nutrition program in order to improve the overall health status of children. Even the severity of child malnutrition remains acute and also it remains stagnant from 2000 to till date. This situation warrants particular attention towards women‟s education to enhance potential productivity of future workforce through mother‟s role at household level, because mother‟s education plays a key role in improving child nutrition within the family environment. The daily calorie consumption sources of drinking water, types of toilet used by the household members, washing mother‟s hand and distance of health centres from the village are the main factors to influence child nutrition. In account of the influences of these determinants, this investigation focuses on the questions how mother‟s education induces child nutrition through providing balance food and maintaining hygiene at household level. The nutritional deprivation between boys and girls by consuming daily calories is also looked at. Thus, the educated mother is expected to be a key actor to improve child nutrition significantly and thus the empirical findings would be useful in formulating well
convinced policy guidelines to improve health and nutrition of Bangladeshi children.
132 In the next chapter, an econometric framework for empirical analysis will be articulated. Questions identified in this thesis to be investigated are: whether mother‟s education influences significantly on i) children‟s school attendance, ii) children‟s nutritional status, or not? Based on these inquiries, models will be formulated to estimate in the following Chapter 5.
133
Chapter 5
Methodology
In Chapter 2, the literature review on women‟s education focused particularly on the indirect contributions generated by the educated women. The chapter examined human capital theory, its determinants and the link between women‟s education and human capital accumulation. It elaborated the economic and social benefits of women‟s education, costs of educating girls, factors associated with lower levels of women‟s education and its consequences. Differential benefits emerging from primary education and post-primary education are also analysed. Generally, educated women participate more in the labour market, which promotes individual‟s income as well as national income through raising productivity. However, in many developing countries, educated women often remain outside the labour market, possibly due to either carry out family responsibilities or living in wealthier families, which do not require women to working outside the home. As a result, women‟s participation in the labour market is lower than that of expected level. Further, the non-monotonic22 pattern of women‟s participation in the labour market essentially raises question about the value of the national priority of investing in girls‟ education. In Chapter 3, an overview of the education system in Bangladesh was presented. The chapter elaborated education policies and strategies undertaken by successive governments of Bangladesh. It highlighted achievements in primary school enrolments, literacy rates and the status of achieving Millennium Development Goals (MDGs) along with the constraints and challenges confronted by the education system. The chapter also described the key education indicators of the South Asian countries such as India, Nepal,
22 In the context of developing countries, women‟s labour force participation may increase at the early stage of age; it tends to decrease at the middle age which is explained by household responsibilities. Again, the participation increases at the latter age. This indicates the non-monotonic pattern.
134 Pakistan and Sri Lanka with regard to Bangladesh‟s relative position in its regional context. It was found from the analysis that in Bangladesh, substantial investment had been made in education sector especially in girls‟ education which had resulted in better outcomes for girls particularly in enrolment rates. Despite these achievements, girls are lagging behind in educational attainment, which lessens their own productivity as well as impacting on the potential productivity of future workforce as educated mothers were shown to put more efforts into their children's educational attainment. This causes substantial losses of human capital in a country like Bangladesh and justifies investing more on girls‟ education. In Chapter 4, the overall health system in Bangladesh and its achievements in various health indicators in relation to Millennium Development Goals were reviewed and illustrated. The chapter provided an analysis of health policies and strategies followed by successive governments as well as the constraints and challenges confronted by the health system. It discussed women‟s health and nutritional status in the context of Bangladesh and compared the health indicators with India, Nepal, Pakistan and Sri Lanka in the regional context. The discussion showed that although the health system is improving in Bangladesh, there remain serious constraints in delivering services to the general public particularly women and children which is manifested in the continued high prevalence of malnutrition. Around half of the Bangladeshi children are stunted and underweight compared to international standard, which implies significant loss of productivity will occur in the future workforce. This context justifies more investment on girls‟ education so that they can improve the health and nutritional standards of their household members.
Despite educated women‟s irregular participation in the labour market, it is evident from the preceding reviews that they inevitably improve the health and nutritional status of their children and also ensure their higher attendance in school. The potential investments that educated women make in their children‟s schooling and improving nutrition produce significant benefits (potential productivity) in the longer term that have great implications from the human capital point of view. However, in most developing countries, girls‟ education is impeded by various socio-economic and cultural setting. These countries, therefore, need to explicitly include the indirect benefits associated with
135 the role played by educated women in the household when assessing their comparative priorities in these areas. Since Bangladesh is trapped into a cycle of low child nutrition and low levels of education, which can cause an enormous wastage of potential productivity for the future workforce, this context motivates an investigation into the questions specifically: i) Whether mother‟s education significantly enhances children‟s school attendance? ii) Whether mother‟s education significantly improves their children‟s nutritional status? By using household level data from Bangladesh, the persuasion of addressing these questions would provide help in policy formulation for investing more in girls' education as a prerequisite to building an educated and healthy workforce.
As pointed out in the introduction in Chapter 1, the methodology used in this thesis involved firstly a review of the literature regarding the impact of women‟s education in terms of both theory and empirical findings in the context of developing countries including Bangladesh (Chapters 2, 3 and 4). The second part of the methodology consists of an empirical investigation in the setting of Bangladesh in relation to impact of mother‟s education on children‟s school attendance and also on their nutritional status in Chapter 6 and Chapter 7 respectively. Econometric models: specifically a probit model is used in estimating educational attainment while a multiple regression model is used for child nutrition. The choices of variables are specified according to the socio-economic context of Bangladesh and available data. In this investigation, data from „Household Income and Expenditure Survey (HIES) 2000‟ and „Child Nutrition Survey (CNS) 2000‟ Bangladesh is used. HIES data includes a community survey data on rural PSU (primary sampling unit) comprising village characteristics. The organization of this chapter is divided into six sections. Section one defines and clarifies the various variables and terms used in this thesis. Section two provides justification for using quantitative approach for empirical investigation in this thesis. Section three discusses the hypotheses developed for empirical investigation. Section four articulates the models used to examine the influences of mother‟s education on
136 children‟s school attendance and also on their nutritional status. Section five describes the regression technique using for estimating the models. Section six discusses sources of data and its limitations while section seven concludes the chapter. It is important to note that the estimated results and explanations of children‟s school attendance and child nutrition are presented in Chapter 6 and in Chapter 7 respectively.
5.1 Explanation of Variables and Terminologies
As described in Chapter 2, the literature on human capital demonstrated that children‟s educational attainment is largely influenced by household factors such as household income or expenditure, land ownership, parents‟ individual level of education, household size, gender of household head and occupation of household head. However, other factors such as availability of school, type of school, quality of teachers, presence of boundary wall, availability of transport and toilet facilities are also relevant (Bellew, Raney & Subbarao 1992; Glick 2008). Student‟s innate ability and quality of school also contribute significantly to learning outcomes (Glewwe & Jacoby 1994). In the case of child nutrition, sources of drinking water, types of toilet used by the household members, nutritional and hygienic practice at home and distance to health centres from the village were identified as having significant affects along with household income and parent‟s education (Hill & King 1993; Gannicott & Avalos 1994).
Clarification of terms and variables is essential due to variation in the inherent meaning of these variables in different contexts. For example, enrolment means the start of the process for achieving educational capability, while literacy rate23 shows the accumulated achievement of this process. Literacy programs impart basic literacy skills to the population thus enabling them to apply such skills in daily life. Literacy rate thus refers to the adult literacy rate considering persons aged 15 years and above. The terminologies of important variables used in this study are explained in the following Table 5.1.
23 Literacy data were collected based on the UNESCO criterion whether a person can „with understanding both read and write a short, simple statement on his everyday life.
137 Table 5.1: Variables in terms of Capability/Input and Functioning/Output
Capability/Input Functioning/Outcome
Enrolment rate: Primary school (I-V) Literacy rate Secondary school (VI-X) Completion of primary level
Secondary School Certificate (SSC) School Attendance Higher Secondary Certificate (HSC) Primary school (I-V)
Secondary school (VI-X)
Household (HH) income Having good health Expenditure on food Being an active worker Intra-household allocation of food to Height -for-age (HAZ) child/HH members Weight-for-age (WAZ) Monthly per capita expenditure (MPCE) Source: Author‟s Articulation based on literature.
Table 5.1 shows that enrolment rate and expenditure are treated as inputs while literacy rate, primary school completion rate, Secondary School Certificate as well as height-for- age and weight-for-age are the outcomes. However, for estimating Equation 5.9 (shown in section 5.4.2), which is developed for educational attainment, „school attendance‟ is used as the dependent variable. Although technically an input variable, here it is used as a proxy variable for educational attainment, which is generated from the data set used in this investigation. In the data set, information regarding the number of children who completed primary school and the number of children who completed secondary school was not available. Due to lack of data, this variable „school attendance‟ is generated from the data set based on level-wise (say primary, secondary) school attendance by the children. 'School attendance‟ for primary school considered the children who belong to the age group 6-10 years and attended classes I-V and secondary „school attendance‟ considered the children who attended classes VI-X and also belong to the age group 11-15 years. In this study, students aged 11-17 years are considered for secondary school to capture more children in this group. The variable „school attendance‟ stands for „educational attainment‟ instead of simply indicating a child‟s presence at school. The widely used term „educational attainment‟ often reflects a wide range of formal and informal learning programs/ processes, but in this thesis, only „formal schooling‟ is considered, again due to data
138 limitations. Clearly, a child‟s presence in a classroom does not guarantee an effective learning process has occurred but it is a prerequisite for any educational attainment to occur. For estimating Equation 5.15 (section 5.4.4) developed for child nutrition, HAZ (height-for-age z-score) and WAZ (weight-for-age z-score) are used as dependent variables to represent whether a child is either stunted or underweight. In this question, a genuine functional outcome is used as the dependent variable. HAZ (height-for-age z score) indicates a score of a child‟s height to measure whether he/she is stunted or not, by comparing it with a standard reference group of National Centre for Health Statistics (NCHS), USA24 for that age group. Similarly, WAZ (weight-for-age z score) indicates a score of a child‟s weight to measure whether he/she is underweight or not compared to the NCHS standard for that age. If the score is positive, this means that child nutrition is improved while a negative score indicates a poor nutritional status. For Bangladeshi children, the values of HAZ and WAZ are usually lower than the international standards and thus the scores of HAZ indicates stunting (low height for age) and WAZ indicates underweight (low weight for age) are negative compared to the international standards. Application of these two common indicators is appropriate in estimating child nutrition as these cover both the short term (as reflected in low weight for age) and the long-term effects (as reflected in low height for age) on nutrition as discussed in Chapter 4.
The uses of „girl‟, „woman‟ and „female‟ need to be clarified as these terms have their own intrinsic meanings. The term „female‟ indicates a common meaning for gender irrespective of age. The term „girl‟ is defined as a female who belongs to the age group of 0-17 years, while woman refers to a female aged above 17 years. Throughout the thesis, the term „girl‟ is used when educational investment is referred. Women and female are used synonymously. 5.2 Justification for Using Quantitative Approach
Studies regarding educational attainment, returns to education, differentials of wages, women‟s fertility and child health and nutrition emphasised the importance of
24 This is also recommended by the World Health Organisation (WHO).
139 using a quantitative approach to analyse these issues (see, for example, Hill & King 1993; World Bank 2005; Asadullah 2006, 2009). Some relevant studies are worth noting in this regard. Ermisch and Francesconi (2001) used a „logit‟ model to analyse the impacts of family background variables on children‟s educational attainments. Kingdon (2002) explained the gender gap in educational attainment in India by using a „probit‟ model. The World Bank (2005) used a multivariate „probit‟ model in the report entitled „Attaining Millennium Development Goals of Bangladesh‟ which investigated several relevant issues related to poverty trends and projections, educational attainment focusing on gender performance, maternal mortality, child and infant mortality and child nutrition using household data HIES 2000 and nutrition data CNS 2000 from Bangladesh. However, most of the previous studies have focused purely on labour market outcomes of women‟s education (Khandaker 1987; Asadullah 2006) ignoring the contributions that educated women made at a household level. Few studies have tried to analyse the impact of mother‟s education on children‟s health and nutritional status and their education (Muhuri 1995; Jahan & Hossain 1998; Lincove 2009). These enquiries are often partial or paid less attention to the significant contributions made by educated women at a household level. The literature thus lacks a single integrated framework to capture the overall impact of women‟s education in developing countries. This thesis tries to bridge that gap by proposing a framework for human capital accumulation which covers two vital factors of productivity enhancement for the future workforce through improving children‟s nutrition as well as their educational attainment. In order to develop pragmatic policy suggestions for improving human capital, the proposed investigation would have significant importance in the context of Bangladesh due to its abundant human resources and very limited natural resource base. 5.3 Hypotheses
Human capital theory argues that parent‟s education has significant influence on children‟s educational attainment (Becker 1962). It is expected that mother‟s education stimulates children‟s education and thus accumulates higher level of human capital by influencing family decision at household level (Sandiford et al. 1995; Zayed et al. 2006).
140 To analyse this argument, following hypotheses are developed:
H1: mother’s education significantly influences children’s school attendance
The second hypothesis is developed on the basis of the argument „does father‟s education motivates son to attend school more or does mother‟s education motivates daughters to attend school more‟. To explore this argument, the hypothesis is written as follows:
H2: there is a positive relationship between father’s level of education and boy’s school attendance and between mother’s level of education and girl’s school attendance
Human capital theory also suggests that mother‟s education has a significant influence on child‟s nutritional status and thus helps to accumulate extensive human capital. To analyse this argument, the hypothesis is constructed as follows:
H3: mother’s education significantly influences a child’s nutritional status in terms of stunting or underweight
The following hypothesis is to develop on the argument that a girl child likely to consumes low calories per day at household level, which leads her to be more malnourished, being either stunted or underweight. In order to explore this argument the hypothesis is developed as follows:
H4: a girl child’s lower calorie intake per day leads to higher probability of being malnourished (either stunted or underweight)
The above hypotheses are analysed through exploring the data set and using econometric models with standard ‘z’ scores and ‘t‟ scores. In predicting the estimated results three levels of confidence intervals: 99%, 95% and 90% are considered.
141 5.4 Model Specification
The specification of model depends on the issues identified to be estimated as well as on the nature of data. If the dependent variable is qualitative and dichotomous - responding „yes‟ or „no‟ to the question under investigation - special treatment is needed to solve the problem instead of using ordinary least square (OLS) regressions. Under such conditions, application of OLS may yield inefficient estimates (Ramanathan 2002). In this context, usually either a „logit‟ model or a „probit‟ model is used (Gujarati 1995). The logit model assumes a logistic distribution of errors while the probit model assumes a normal distribution of errors but is otherwise similar. The main features of these models are described in the following sections.
5.4.1 The Logit and the Probit Models
I. The Logit Model
In a binary response model, interest lies in the probability response. The model that emerges from the cumulative distribution function (CDF) is popularly known as a „logit (logistic) model‟. The simple regression model is formulated as:
Yi X i u i 5.1
where, Yi shows whether or not to pursue a particular course of action, the
observed value of Yi is thus either 1 or 0; Xi are the explanatory variables
and ui is the error term.
The logit (logistic) model, which is also known as log odds ratio (Sharma 1996), has the following functional form (omitting subscript i):
P ln Xu 5.2 1 P
142 If, P is binary e.g. takes value either 0 or 1, then the logarithm of PP/(1 ) is undefined. The procedure used in such a case is the maximum likelihood method (Ramanathan 2002, p532-533). Therefore, the marginal effects of X on P are calculated by taking the partial derivative of P with respect to X. The estimated marginal effect is given as follows:
ˆ ˆ ()ˆ ˆ X Pe ˆ ˆˆ PP(1 ) 5.3 X [1e (ˆ ˆ X ) ] 2
This is a commonly used practice in econometric analysis, which shows the effect of a certain variable holding the effects of other variables constant.
II. The Probit Model
The model that comes from a normal cumulative distribution function is known as a „probit model‟, which assumes a response function of the form: Yi X i u i ,
ui where Xi is observable but Yi is an unobservable variable. has the standard normal distribution. So, Yi, takes the value 1 if > 0 and 0 otherwise (Ramanathan 2002). Thus,
Yi = 1 if Xui > 0 5.4
Yi = 0 if Xui 0 5.5
If it is denoted by F(z), the cumulative distributive function of the standard normal distribution, that is F(z) = P(Z z), then
X i P( Yi 1) P ( u i X i ) 1 F ( ) 5.6
X i P( Yi 0) P ( u i X i ) F ( ) 5.7
The joint probability density of the sample of observations (called the likelihood function) is therefore given by:
143
XX LFF(ii ) 1 ( ) 5.8
YYii01
Where denotes the product of terms. By maximising the expression, which is non- linear in its parameters, α and β are estimated (Ramanathan 2002) and thus marginal effects are obtained easily.
However, marginal effects for discrete dependent variable measures the expected instantaneous change in the dependent variable keeping all the covariates constant (SAS Institute Inc. 2011). One of the approaches to estimate marginal effect is to compute the marginal effects at sample means of data. Marginal effects at sample means of covariates can be obtained by adding an observation with a missing value for grades or dummy variables. However, the current practice is to compute marginal effects at all sample points and then compute mean across these as the average marginal effects, this may not be too different than the marginal effects evaluated at sample means.
For many economic issues and problems, either logit model or probit model are used as standard quantitative technique although results are almost similar despite the different underlying assumptions. However, in practice, different estimation techniques generally make no (or negligible) differences among the estimates (Ainsworth, Beegle & Nayamete 1996). In this thesis, use of probit model is expected to answer sufficiently the problems identified as: whether mother‟s education significantly influences children‟s school attendance or not? In addition, the model has the advantage of controlling the effects of several variables that are simultaneously associated with children‟s school attendance (World Bank 2005).
5.4.2 Model for Educational Attainment
In educational attainment, children‟s „school attendance‟ (for primary and secondary level) is used as the dependent variable. Completion of primary school data as well as secondary school data were not available in the data set as stated before, which motivated to generate a new variable called „school attendance‟ and this is used as a
144 proxy variable for educational attainment. It is a dichotomous qualitative variable taking the value „1‟ for school attendance and „0‟ for non-attendance.
The explanatory variables considered in the model are: household income, father‟s and mother‟s individual levels education, interaction variable: father‟s education with boy child and mother‟s education with girl child, female-headed household, girl‟s status in school attendance compared to boy, year-wise age of child, average expenditure of schooling, distance to school from the village and supply of electricity to the rural household (as discussed in chapter 2). The model is constructed as follows, letting ‘S’ be „school attendance‟:
Si* = α1+α2MPCEi+ α3FEi+ α4MEi+α5FEi*b+α6MEi*g+α7FHHi+
α8Sex2i+ α9 ACi + α10AEi + α11 DSi + α12SEi + Ui 5.9
With, Si* = 1 if Si* > 0 and 0 otherwise; Thus, Si* = 1 indicates school
attendance for an individual child and Si* = 0 indicates non - school attendance.
Where,
MPCE : Indicates monthly per capita consumption expenditure of household which is used as a proxy variable of household income FE : Father‟s level of education
ME : Mother‟s level of education
FE*b : Indicates interaction between father‟s education with boy child
ME*g : Indicates interaction between mother‟s education with girl child
FHH : Indicates female-headed household
Sex2 : This indicates girl‟s status at school attendance compared to boy
AC : Year-wise age of children
AE : Average expenditure of schooling
145 DS : Distance to school from the village
SE : Supply of electricity to the household
U : Stochastic disturbance term, and
‘i’ : Indicates i th individual.
In Equation 5.9, α1 indicates constant term, which shows the estimated average value of the dependent variable when all explanatory variables are zero. On the other hand, the coefficients from α2 to α12 show the estimated change in the average value of the dependent variable for each unit change in an independent variable holding all other explanatory variables constant (Albert 2006). The expected signs of the coefficients α2 to
α12 of explanatory variables are explained below:
α2 - is the coefficient of Monthly Per Capita Expenditure (MPCE): This coefficient shows the response of school attendance due to change in monthly per capita expenditure (MPCE). Children‟s school attendance is positively associated with household income (MPCE is a proxy variable of income) meaning children attend school more if households‟ income increases. Thus, the sign of this coefficient is expected to be positive.
α3 - is the coefficient of Father’s Education (FE): It shows how much school attendance responds due to one unit change in father‟s education. Five levels of education is considered in the case of parent‟s education. However, father‟s level of education (FE) usually positively influences children‟s school attendance and thus the expected sign of this coefficient is to be positive.
α4 - is the coefficient of Mother’s Education (ME):
In a similar connotation, mother‟s levels of education (ME) positively influence children‟s school attendance and thus the expected sign of this coefficient would be positive.
146 α5 - is the coefficient of Father’s Education* boy child (FE*b):
This coefficient indicates the responsiveness of potentially a real relationship embedded between father‟s education and boy child (FE*b). The level wise interaction dummy is used in this case. The sign of this coefficient is expected to be positive as father‟s
education affects boy‟s school attendance positively.
α6 - is the coefficient of Mother’s Education *girl child (ME*g): Similarly, the coefficient indicating interaction between mother‟s education with girl child (ME*g) would have positive relation. Thus the sign of these coefficients are expected be positive. The level wise interaction dummy is used in this case.
α7 - is the coefficient of Female Headed Household (FHH): Female-headed households usually have insufficient incomes to provide children‟s essentials needed for schooling and thus negatively impact on school attendance. The sign of this coefficient indicating female-headed household (FHH) is expected to be negative.
α8 - is the coefficient which indicates Girl’s Status in School Attendance compared to Boy (Sex2):
This coefficient of Sex2 shows the response of girl‟s status in school attendance compared to boy, as boy (Sex1) is the base category. Although, girl‟s status in school attendance is expected to be negative but in practice, due to being implemented various policies and programs for girls‟ education particularly at primary and secondary schools, the response might be positive. Thus the sign of this coefficient may be either positive or negative.
α9 - is the coefficient of Age of Child (AC): The response of coefficient regarding age of child is expected to be positive for each year from 6 to 10 years for primary school attendance. On the other hand, in the case of secondary school, the
147 sign is expected to be positive for each year from 11 to17 years. However, since most students in Bangladesh take more years than the officially determined year to complete primary school, in the cases of year11 and year12, the responses for primary school attendance might be positive while the responses for secondary school attendance might be negative. This is explained in detail in section Chapter 6, section 6.3.5.
α10 - is the coefficient of Average Expenditure of Schooling (AE): Usually, the response of school attendance due to change in average school expenditure is negative because higher school expenditure reduces children‟s school attendance and vice versa. However, in practice in Bangladesh, since poor families cannot afford higher expenditure for their children‟s schooling, in order to support them, various programs are implemented by the government, such as, free text book distribution, free tuition fee, stipend program for all children at primary school and stipend for girls up to class VIII etc. These programs reduce the expenditure for schooling and thus have significant impacts on children‟s school attendance. Therefore, the expected sign of this variable may not be negative. Besides, there remains an argument that higher quality education incurs higher expenditure and thus has positive association. Therefore, the sign of expenditure for schooling may be either positive or negative.
α11 - is the coefficient of Distance to School from the Village (DS): The response of the variable „distance to school from the village‟ is expected to be negative because of the inconvenience of appropriate transportation for attending schools located at distant places. This however, reduces the children‟s school attendance particularly in the rural areas and thus the expected sign would be negative.
148 α12 - is the coefficient of Supply of Electricity (SE): The response in school attendance due to connection of electricity to rural household is expected to be is positive. In the rural areas, electric connection plays a great role in student‟s academic performance, which encourages them to attend school more. Due to electricity connection to the households, students can spend more time on study and preparation of homework and thus keep them on track in performing academic activities which motivated them to attend school more and stay school longer.
It is important to note that the Equation 5.9 assigned for school attendance is solved for i) primary school attendance, ii) secondary school attendance. In both cases, models are also estimated from the national perspective as well from the rural perspective. Therefore, rural specification includes two more independent variables: distance to school from the village and supply of electricity to the rural households indicating rural characteristics. However, detailed explanation of the independent variables considered in Equation 5.9 is given in Table 5.2.
149 Table 5.2: Elaboration of Independent Variables used in Equation 5.9 Independent Variables Elaboration
Household Income (MPCE) Monthly per capita expenditure, which is used as a proxy variable of household income. It is a continuous variable. Father’s Education (FE) Primary level Education dummy is used for father‟s education for five Lower secondary levels. The levels are: no education, primary education, Higher secondary lower secondary, higher secondary, graduate and above level. Graduate & above `No education‟ is used as reference level.
Mother’s Education (ME) Education dummy is used for mother‟s education for five Primary level levels. The levels are: no education primary level, lower Lower secondary secondary, higher secondary, graduate and above level. Higher secondary Again, „no education‟ is used as reference category. Graduate and above
Father’s Education x boy (FE*b) Interaction of father‟s education with boy child indicates the Boy * Primary level synergistic influence of these two factors on children‟s Boy * Lower secondary school attendance. Education dummy is used for five Boy * Higher secondary education categories and „no education‟ is used as reference Boy * Graduate and above level.
Mother’s Education x girl (ME*g) Interaction of mother‟s education with girl child indicates the Girl * Primary level synergistic influence of these two factors on children‟s Girl * Lower secondary school attendance. Education dummy is used for five Girl * Higher secondary education categories and „no education‟ is used as reference Girl * Graduate and above level.
Head of Household (HH) Dummy variable is used for male and female: „1‟ for female- Male Headed (reference category) headed (FHH) household and „0‟ for male-headed (MHH) Female Headed household.
Girl’s Status in Attendance (Sex2) The variable Sex2 indicating girl‟s response in school Boy (Sex1-reference category) attendance compared to boy (Sex1) as boy is the base Girl (Sex2)
category. Thus, in this case Sex2 = 1 and Sex1 = 0
Age of Child (AC): Primary School
Year 7 Year-wise age dummy from 6 to 10 years is used for primary Year 8 school attendance. Year-6 is used as the reference year. Year 9 Year 10
Age of Child (AC): Secondary School Year 12 Year 13 Dummy for each year of age from 11 to 17 years is used for Year 14 secondary education (11-17 years). Year 11 is used as the Year 15 reference year. Year 16 Year 17
Average Expenditure of Schooling (AE) Average expenditure of schooling (in local currency) is estimated based on level wise expenditure and used as a RHS variable. It is a continuous variable. Average distance to school from the village measured in Distance to School (DS) kilometre and it fell into „yes‟ or „no‟ category. .
Supply of Electricity (SE) The variable electricity connection to rural household fell into „yes‟ or „no‟ category. Source: Author‟s Articulation based on HIES, 2000 including community survey.
150 5.4.3 Multiple Regression Model
A model in which the dependent variable Y depends on two or more explanatory variables is called a multiple regression model. A simple three-variable regression model is as follows:
Yi1 2 X 2 i 3 X 3 i u i 5.10
Yi is the dependent variable, X2, X3 are the explanatory variables, ui is the stochastic disturbance terms, and i is the ith observation. β1 is the intercept term, which gives the mean or average effect on Yi if all the explanatory variables are excluded from the model. β2 and β3 are called the partial regression coefficients (Gujarati 1995). Basic assumptions of regression models are presented below.
In regression analysis, interval estimation is based on a normality assumption ˆ ˆ regarding ui. Since both ordinary least square (OLS) estimators 1 (constant) 2
(coefficient) are linear functions of ui, which is random by assumption, the probability distributions of the OLS estimators will also be normally distributed as ui. The normality assumption is that each ui is distributed normally with:
i. Mean: E (ui |Xi ) = 0 5.11
ii. Covariance: Cov (ui ,uj) = 0 i j 5.12
2 iii. Variance: Var (ui|Xi) = σ 5.13
2 The assumption stated as: ui ~ N (0, σ ) means each ui is normally distributed with 2 zero mean and a constant variance referred as σ . E (ui |Xi ) = 0 implies that the mean value for each ui is zero and Cov(ui, uj) = 0, i j indicates no serial correlation 2 among ui and uj while (ui|Xi) = σ indicates variance of ui is homoscedastic (Gujarati 1995, 2003). Other important assumptions of multiple regressions are:
151 iv) Cov ( ui, Xi) = 0 5.14
which indicates zero covariance between ui and each Xi variable;
v) no specification bias exists or the model is correctly specified; and
vi) no exact collinearity between the Xi variables.
The term multicollinearity means the existence of a „perfect‟ or exact linear relationship among some or all explanatory variables of a regression model. The classical linear regression model assumes that there is no multicollinearity among the X variables, because if multicollinearity is perfect, the regression coefficients of the X variables are indeterminate and their standard errors are infinite. If multicollinearity is less than perfect, the regression coefficients although determinate, possess large standard errors, which means the coefficients cannot be estimated with great precision or accuracy (Gujarati 1995). In multiple regression analysis, it is difficult to interpret the estimates of the individual coefficients if the variables are highly correlated. The multicollinearity problem depends on the magnitude of the variances of the errors and explanatory variables. When an exact linear relationship exists between two or more explanatory variables, the regression coefficients corresponding to these independent variables cannot be uniquely estimated. However, the description on multicollinearity provides (Discrete Dependent Variable Model n.d.) that the essence of high collinear variables can cause regression parameters to be inefficient and also can cause the signs of regression coefficients to be counter-intuitive.
Multicollinearity could be identified by examining the pattern of correlation among explanatory variables. Since time series variables tend to grow together, models based on time series are more subject to multicollinearity problems than those of cross- section models. However, no single solution can eliminate the problem of multicollinearity. If similar variables are presented in the model, eliminating redundant variable is recommended and the possible candidates are those with very low t-statistics (Ramanathan 2002). Other solutions include Ridge regression and principal component regression. In addition, considering more observations (data) and examining the prior
152 information are helpful to reduce multicollinearity (Maddala 2002). Furthermore, description on the multicollinearity defines that quantifies the severity of multicollinearity in an ordinary least squares regression analysis, provides an index (variance inflation factor) that measures how much the variance of an estimated regression co-efficient is increased because of collinearity (Variance Inflation Factor n.d.). Correlation matrix is also useful to identify multicollinerity. If the correlation of an independent variable with dependent variable is positive, meaning that if the value of a predictor goes up, the value of explained variable also tends to go up and vice versa provided by UCLA Academic Technology Services (2011).
In deriving ordinary least square (OLS) estimates, one of the major assumptions is identical distribution of ui with mean zero and equal variance σ, which is known as 2 homoscedasticity. The variance (σ ) is a measure of dispersion of the error terms (ui) around their mean (zero). In cross-section data, especially, this assumption might be violated and there is a possibility of heteroscedasticity (Ramanathan 2002).
Due to the heteroscedasticity problem, the unbiased least square estimates become inefficient and the estimated variances can be biased. Thus, it is necessary to identify the existence of heteroscedasticity in the data set. Ramsey‟s test, Glejser‟s test, Breusch and Pagan‟s test, White‟s test, Goldfeld and Quandt‟s test and the likelihood ratio test are important in this regard (Gujarati 1995). Moreover, a useful device for 2 identifying heteroscedasticity is to graph the square of OLS residuals E(ui ) against variables that are suspected of causing heteroscedasticity or against the fitted values (Gujarati 1995).
Maddala (2002) indicated that two solutions are commonly used for correcting the error variances: i) use of weighted least squares, and ii) transforming the data into the logarithmic form which has received considerable attention. In the case of cross-section data, it is noted that when transformation of data does not eliminate or alternate the leverage of influential outliers that cause bias in prediction and distort the significance of estimated parameters, robust regression could be a useful tool (Yaffee 2002, p1). Albert (2006) noted that the „robust regression’ procedure generates estimates and standard errors different from those obtained from the OLS procedure. It is used when there is
153 concern about outliers. In the cross-section household level data, ‘robust regression‟ is used to minimise the influence of outliers on estimates. Multiple regression models, however, show the partial effects of one variable on the dependent variable when controlling for the effect of other variables on the basis of the above stated assumptions.
5.4.4 Child Nutrition Model
In the case of child nutrition, two multiple regression models are estimated: one for HAZ (stunting) and another for WAZ (underweight) and thus HAZ and WAZ are used as dependent variables. As these variables are quantitative, multiple regression model is used in estimating child nutrition. Explanatory variables included in the model are: daily per capita calorie consumption, father‟s and mother‟s individual level of education, girl‟s status in nutrition compared to boy‟s, sources of drinking water, types of toilet used by the household members, washing mother‟s hand and distance to health centres from the village. It is worth noting to mention that household income is essentially an important variable as a determinant of child nutrition. In the original model for nutrition, household income was included as an explanatory variable and the effect of income on HAZ and WAZ were significant. But when the variable daily per capita calorie consumption was included, the effect of income became insignificant although the effects were separately positive and significant. Daily calorie consumption is highly correlated with household income and plays a vital role in improving child nutrition. However, it is evident in Bangladesh that even in a wealthy family, daily calorie consumption by a child is not necessarily ensured due to lack of knowledge regarding nutrition and hygiene at household level (as discussed in Chapter 4). Thus, higher household income does not necessarily improve child nutrition. In order to have a vigorous effect of calorie consumption per person (per child) at household level, daily per capita calorie consumption (DCC) is considered as one of the most useful variable instead of household income in this thesis.
154 The specification of multiple regression model is shown below:
CNi = β1 + β2DCCi + β3FEi+ β4MEi + β5 Sex2i+ β6Dri + β7TTi+ β8WHi+
β9HCi +Vi 5.15
Where, CN : Indicates child nutrition measured in HAZ (height) and WAZ (weight). DCC : Daily per capita calorie consumption FE : Father‟s education ME : Mother‟s education Sex2 : Girl‟s status in nutrition (height and weight) compared to boy Dr : Sources of drinking water TT : Types of toilet used by the household members WH : Washing mother‟s hand after defecation HC : Distance to health centres from the village V : Stochastic error term i : Indicates i th individual
However, in Equation 5.15, the term β1 indicates a constant, which shows the estimated average value of the dependent variable when all explanatory variables are zero. The coefficients, β2 to β9, show the estimated change in average value of dependent variable for each unit increase in an independent variable holding all other explanatory variables constant (Albert 2006). The expected signs of the coefficients are given below:
β2 - is the coefficient of Daily per capita Calorie Consumption (DCC):
This coefficient shows the responsiveness of daily per capita calorie consumption (DCC) by an individual household member. A certain level of daily calorie consumption improves a child‟s nutritional status and thus the response is expected to be positive. But if a minimum level of daily calorie consumption is not maintained, the expected sign may not be positive.
155 β3 - is the coefficient of Father’s Education (FE): Individual level of father‟s education (FE) positively influences child nutrition i.e. if father‟s education of a child increases, it improves child nutrition. Thus, the sign of the coefficients of father‟s all levels of education are expected to be positive. As similar to school attendance, five levels of father‟s education are considered in this case.
β4 - is the coefficient of Mother’s Education (ME): In assessing the effects of mother‟s education on child nutrition, like father‟s, five levels of mother‟s education are considered. However, individual level of mother‟s education (ME) positively influences child nutrition and the sign of the coefficient is expected to be positive.
β5 - is the coefficient of Girl’s Nutritional Status compared to Boy (Sex2): The coefficient of Sex2 shows the response of girl‟s status in nutrition compared to boy since boy is used as base category. Girls are often neglected in feeding and providing treatment and other necessities in the childhood, thus, the sign of this coefficient is expected to be negative.
β6 - is the coefficient of Sources of Drinking Water (Dr)
The role of sources of drinking water (Dr) is very important particularly for child health and nutrition. Safe sources of drinking water improve child nutrition while non-safe sources deteriorate the children‟s nutritional status. Based on this intuition, the sign of the coefficient would be either positive or negative.
β7 - is the coefficient of Types of Toilet (TT):
In a similar pattern of sources of drinking water, hygienic toilets (TT) used by the household members affect child nutrition positively and non-hygienic toilets affect negatively. Thus, the sign of the coefficient would be either positive or negative depending on the type of toilet.
156 β8 - is the coefficient of Washing Mother’s Hand (WH):
Washing mother‟s hand (WH) after defecation plays a vital role in improving child health and nutrition. Otherwise, it is detrimental to child health and nutrition. Therefore, the expected relation between the two is positive and thus the sign would be positive.
β9 - is the coefficient of Distance to Health Centres (HC):
Distance to health centres (HC) from the village has negative influence on child nutrition. If health centres are far from the village, children do not receive proper treatment in time. Thus, the expected relation between distance to health centres and child nutrition would be negative and thus the sign of the coefficient is expected to be negative.
It is important to note that the model is estimated i) for the probability of a child being stunted (HAZ), and ii) for the probability if a child being underweight. In both cases, the model is estimated from the national perspective and also from the rural perspective. The rural specification includes one more independent variables „distance to health centres‟ from the village indicating village characteristics. The detailed explanations of all variables are given in Table (5.3).
157 Table 5.3: Elaboration of Explanatory Variables Used in Equation 5.15 Independent Variables Elaboration
Daily per capita Calorie Consumption Daily per capita calorie consumption is measured in kilo- (DCC) calorie consumed by person per day. The standard daily calorie consumption is 2122 kilo-calorie per person.
Father’s education (FE)
No education (reference level) Dummy variable is used for father‟s levels of education. Primary level Like school attendance function the levels are: no Lower secondary education, primary education, lower secondary, higher Higher secondary secondary and graduate and above. „No education‟ is Graduate & above considered as reference level.
Mother’s Education (ME) Dummy variable is used for mother‟s different levels of No education (reference level) education: Similarly, the levels are: no education, primary Primary level education, lower secondary, higher secondary and graduate Lower secondary and above. „No education‟ is considered as reference level. Higher secondary Graduate and above
Girl’s Status in Nutrition (Sex2) The variable Sex2 indicates girl while Sex1 indicates boy. Boy (Sex1-reference category) If Sex2 is 1 indicating girl‟s response in nutrition compared Girl (Sex2) to boy because boy is considered as the base category.
Sources of Drinking Water (Dr) Dummy variable is used for five individual source of Tube-well (deep) drinking water. Pond/ river water is used as reference Tube-well (shallow) source. Tap water Well (small and deep pond) Safe source of water: tube-well (shallow), tube-well (deep) Pond/ river (reference source) tap water and well. Non-safe source of water: pond /river.
Types of Toilet (TT) Dummy variable is used for six individual type of toilet. Flush/sanitary/water-sealed latrine Fixed-kucha latrine is used as reference toilet. Pit latrine (closed) Pit latrine (open) Hygienic toilet: flush/sanitary/ water-sealed latrine, pit Fixed-kucha25latrine (reference toilet) latrine (closed), and Hanging latrine Non-hygienic toilet: pit latrine (open), fixed-kucha latrine, Open space (no fixed place) hanging latrine and open space.
Washing Mother’s Hand after Washing mother‟s hand fell „yes‟ or „no‟ category. Defecation (WH)
Distance to Health Centres from the Average distance to health centres from the village is Village (HC) measured in kilometre and it fell „yes‟ or „no‟ category.
Source: Author‟s Articulation, based on Household Income and Expenditure Survey 2000 and Child Nutrition Survey 2000.
25 The word „Kucha‟ means the establishment made of bamboo or wood which is not very strong.
158 5.5 Regression Technique
5.5.1 Estimation of Children’s School Attendance
Based on Equation 5.9, two separate models are estimated: one for primary school attendance, and the other for secondary school attendance. School attendance regressions are performed at the national level to pick up the differential status of these two important levels of education in Bangladesh. Moreover, in both cases, village characteristics e.g. distance to school from the village, supply of electricity to rural households are included to examine the school attendance from the rural perspective. These two variables distance to school from the village and supply of electricity to the rural households were picked up from the Community Survey, which was included in Household Income and Expenditure Survey (HIES) 2000 data set.
School attendance function is estimated using the probit model since school attendance is a „binary variable taking the value „1‟ for school attendance and „0‟ for non- attendance. The probit estimate shows the marginal effects on the dependent variable i.e. change in the probability for an infinitesimal change in each independent continuous variable. This also reports the discrete change in the probability of dummy variables by default. In estimating probit model (Equation 5.9), differential probit (dprobit) command is used, which reports the marginal effects on the dependent variables evaluated at sample means. However, marginal effects at sample means of covariates can be obtained by adding an observation with a missing value for grades or dummy variables (SAS Institute Inc. 2011).
However, since cross-section household survey data is used for school attendance function, there may be the possibility of multicollinearity, selection bias and heteroscedasticity in estimating the model. How to overcome these problems are explained below.
I. Multicollinearity
The essence of highly collinear variables (e.g. parent‟s income and parent‟s education are likely to be highly correlated) can cause the regression parameters to be inefficient as stated and thus it is difficult to have independent effects of the correlated variables parent‟s income and parent‟s education on children‟s school attendance by
159 estimating the model. Therefore, for major RHS variables: household income, parent‟s individual levels of education, gender differential in school attendance and year-wise age of child, two tables for variance inflation factors (VIFs) are calculated based on Equation 5.9 (as shown in Appendix Table A8.5 for primary school and Table A9.5 for secondary school). In both cases, it is observed from the tables that all explanatory variables have relatively low VIF values and these do not exceed the cut off26 value 10. Moreover, the average VIF value for primary school attendance is 1.28 and it is 1.26 for secondary school attendance. These indicate that multicollinearity is not a matter of concern in any case. In relation to multicollinearity, the correlation matrices from national perspectives are also constructed and these are enclosed in Annexure B5 (Table B5.1 and Table B5.2). It is seen from the tables that household income, parent‟s education have positive correlations with children‟s school attendance. Average expenditure of schooling is unusually highly negatively correlated with primary school attendance while it has reasonable positive correlation with secondary school attendance. Age of child particularly year 8 to year 10 have positive correlation with primary school attendance. In the case of secondary school, year 13 to year 17 have positive correlation. However, year 11 and year 12 have negative correlations in attending secondary school. Knowing that these variables are correlated with school attendance, it might predict that they would be statistically significant predictor variables in the probit model.
In addition, to address the multicollinearity problem, less important variables were eliminated from the Equation 5.9 through examining the data set. For example, in school attendance, two variables: monthly per capita expenditure (MPCE) and monthly education expenditure (MEC) of household - both are important and also relevant for the model. But, since the effect of MPCE (proxy of income variable) on school attendance is usually stronger than that of MEC - which counts household‟s education expenditure only, and thus, MPCE was included in the model as an independent variable instead of MEC. This process was persuaded in selecting other variables also.
26 There is no formal cut off value to use with VIF for determining presence of multicollinearity, values of VIF exceeding 10 are often regarded as indicating multicollinearity, but in weaker models, values above 2.5 may be a cause for concern (Laura & Simon 2004).
160 Moreover, there may be a correlation between father‟s and mother‟s education due to similar family characteristics. Usually, an educated woman will marry an educated man and thus, they have higher household income and also spend more on their children‟s education, which may bias the estimate of school attendance. Thus, the unobserved characteristic that forms the basis of this associative mating (multicollinear type of problem) could be addressed by using an instrumental variable (IV) (Brierovia & Duflo 2002). But in the data set used in this thesis, it is very difficult to find an instrumental variable to address this concern. Besides, there may be a possibility of endogeneity bias which occurs when an unobserved (potential) Right Hand Side variable is correlated with a Left Hand Side variable. For example, mother‟s membership of non- government organisation (NGO) may influence children‟s school attendance. In this case also, an instrumental variable which fits 2SLS method is used to solve endogeneity bias. Again, it is very difficult to find a suitable instrumental variable to address this problem. For example, if grandparent‟s education of a child is considered as an instrumental variable to capture the endogeneity bias, but in the data set, a few number of grandparents of the children were found as educated, which may not capture the endogeneity bias.
II. Selection Bias Sample selection bias arises mainly from considering the non-random variable in the model and describes a systematic difference in characteristics between those who are selected for study and those who are not. In this thesis, since two separate models (primary as well as secondary school attendance) are estimated, in order to select children for secondary school, sample selection bias may arise because of children who appear in secondary school must have had to attend primary school as well. Therefore, the determinant of secondary school attendance cannot be independent of primary school attendance. In other words, individuals in secondary school, which is a fraction of the total cohort, thus have selection bias due to unobserved ability or motivation or may be parental characteristics of individuals, which are not captured by the individual‟s secondary school attendance function (Equation 5.9). In such a case, an instrumental variable could be used to capture the unobserved influences. But again, it is very hard to find an instrumental variable to capture these unobserved effects in estimating the model. However, the web discussion on selection bias presents that selection bias affects the
161 external validity of a study, not its internal validity (Selection Bias n.d.). Thus, in cross section household survey data used in this thesis, the question of selection bias may not be very dominant.
III. Heteroscedasticity
In this thesis, cross-section household data are used, in which, there is a concern of heteroscedasticity problem. In estimating the school attendance function, „robust regression‟ is used to minimize the standard errors in order to address this problem.
IV. Discussion of Hypotheses
In the case of hypothesis H1: mother’s education significantly influences children’s school attendance - was examined by the respective z-scores of various levels of education based on Equation 5.9. The probit model, which is used for school attendance function, apparent assessment for the coefficient is given by the respective z- score. In the case of H2: there is a positive relationship between father’s level of education and boy’s school attendance and between mother’s level of education and girl’s school attendance - to explore this intuition, z-scores of level wise interaction variables: father‟s level of education with boy child (FE*boy) and mother‟s eduction with girl child (ME*girl) are examined and find out the significance of estimates.
5.5.2 Estimation of Children’s Nutritional Status
Based on Equation 5.15, two separate models are estimated: one for HAZ (stunting), and the other for WAZ (underweight). In order to pick up long term effect on child nutrition manifested by HAZ and short term effect manifested by WAZ from national perspective, these models are estimated. Child nutrition status from rural perspective is also estimated considering community servery data. Since, HAZ and WAZ are known as quantitative variables, a multiple regression model is used and it is estimated through using OLS method. In child nutrition, household level child nutrition survey (CNS) data is used while household characteristics are taken from the HIES data and importantly, community survey data is included in HIES. In this cross section
162 household data set, multicolliniarity and heteroscedasticity again are the matters of concerns and how to overcome these concerns are explained below.
I. Multicollinearity
In estimating child nutrition in terms of HAZ and WAZ, it is attempted to include relevant variables in the model (Equation 5.15) by exploring the coefficients of similar variables and by leaving out the relatively less important variables determined by low t- scores. Besides, there may be a correlation between father‟s and mother‟s education which may influence child nutrition. This associative mating again, possibly due to unobserved household characteristics could be addressed by using an instrumental variable as suggested by Brierovia and Duflo (2002) but again, it is very difficult to find a suitable instrumental variable from the data set to address this problem. However, in order to diagnose multicollinerity, a variance inflation factor associated with major RHS variables of Equation 5.15 such as daily per capita calorie consumption, parent‟s individual level of education, girl‟s comparative nutritional status, sources of drinking water, types of toilet and washing mother‟s hand are estimated. Interestingly, tables reporting VIFs for HAZ are exactly same for WAZ, which may be due to using the same variable and also same number of observations (children) in both cases. Thus, one VIF table for child nutrition is included in Appendix Table A10.5. It is observed from the table that most of the variables have relatively low values of VIFs and the average value for HAZ/WAZ is 2.19. In the case of drinking water, all sources except water from well have the values of VIFs more than 5 and particularly tube-well (deep) water has the highest value of 8.47, which is relatively large. However, since these values are not exceeding the cut off value of 10, it can be said that multicollinearity is not a severe problem in the estimation of child nutrition. Furthermore, correlation matrices are also constructed for child nutrition in terms of HAZ and WAZ which are enclosed in Annexure B5: Tables B5.3 and B5.4. From the correlation tables, it is seen that child nutrition is comparatively highly positively related with daily calorie consumption per person, both father‟s and mother‟s higher secondary and above levels of education and sanitary/flush/water sealed toilet used by the household members. It is also seen that girl‟s status indicated by Sex2 is negatively correlated with
163 both HAZ and WAZ. Knowing that these variables are correlated with child nutrition (HAZ and WAZ), it might predict that they would be statistically significant predictor variables in the multiple regression model. II. Heteroscedasticity
Since cross-section household survey data is used to estimate child nutrition, there is a concern of heteroscedasticity problem in the data set. Thus, in estimating multiple regression model (Equation 5.15), robust regression is used to minimise the standard error. III. Regarding Hypotheses and Beta Coefficient
In the case of child nutrition, the hypothesis H3: mother’s education significantly influences a child’s nutritional status in terms of stunting or underweight - is examined by t-scores based on Equation 5.15. The apparent significance of the effects of mother‟s education on both HAZ and WAZ would also be sorted. In order to compare the relative strength of various predictors, particularly mother‟s and father‟s individual levels of education, „beta coefficient‟ is estimated for the restricted model considering only parent‟s individual levels of education (Equation 5.15). Beta coefficient actually shows the same standardised unit of (raw) coefficient by which regression coefficient could be compared to assess the relative strength of each predictor within the model (UCLA Academic Technology Services 2011.) and thus could easily be identified the comparative influence of father‟s or mother‟s education on child nutrition.
The other hypothesis H4: a girl child’s lower calorie intake per day leads to a higher probability of her being malnourished (either stunted or underweight) – indicates the differential effects of daily calorie consumption which is estimated by sorting boys and girls‟ daily calorie consumption separately. The correlation table (as shown in Annexure B5, in Tables B5.3 and B5.4) shows that girls status in calorie consumption is negative. In both cases of children‟s school attendance and child nutrition, statistical package Stata Version 10 is used to estimate the models, manipulate data and integrate
164 household characteristics with community data at the village level as well as with child nutrition data.
5.6 Sources of Data
5.6.1 Survey Design The data set to be used in this analysis was collected from the „Household Income and Expenditure Survey (HIES) 2000‟. HIES is a national survey conducted by the Bangladesh Bureau of Statistics (BBS). Multi stage stratified random sampling technique was followed in drawing the sample for HIES 2000 under the framework of an Integrated Multipurpose Sample (IMPS), design developed on the basis of the population census. The IMPS consists of 442 primary sampling units (PSUs) throughout the country. There are 252 rural and 190 urban PSUs in the sample. Each PSU comprises around 20 households for rural and urban municipal areas. Each PSU for statistical metropolitan areas (SMAs) comprises only 10 household. These are shown in Table 5.4 below.
Table 5.4: Primary Sampling Units (PSUs) by Stratum: 2000 Area Bangladesh (PSU) Households (HH)
Rural 252 (20 HH per PSU) 5040
Urban 1000 i) Municipality 50 (20 HH per PSU) 1400 ii) SMA 140 (10 HH per PSU)
Total 442 7440
Source: The report of Household Income and Expenditure Survey 2000, Bangladesh Bureau of Statistics (BBS), Ministry of Planning, Government of Bangladesh, Dhaka.
The HIES, 2000 survey covers the whole of Bangladesh, which consists of 6 administrative divisions, 64 districts and about 507 thana (sub-district) (BBS 2006b). The survey was conducted over 14 regions27 covering all administrative divisions and districts. Thus the survey contains data on 7,440 households with 38,515 members, which
27 The areas are covered by 14 statistical regions known as 1= Dhaka Statistical Metropolitan Area (SMA), 2 = Other urban areas of Dhaka Division, 3 = Rural areas of Dhaka and Mymensigh, 4 = Rural areas of Faridpur, Tangail and Jamalpur, 5 = Chittagong SMA, 6 =Other urban areas of Chittagong Division, 7 = Rural areas of Sylhet and Commilla, 8 = Rural areas of Noakhali and Chittagong, 9 = Urban area of Khulna Division, 10 = rural Areas of Barisal and Patuakhali, 11= Rural area of Khulna, Jessore and Kustia, 12= Urban areas of Rajshahi, 13= Rural areas of Rajshahi and Pabna, 14= Rurla areas of Bogra, Rangpur and Dinajpur and grater districts.
165 includes 5,040 rural households and 2,400 urban households. Weights were used to ensure the survey results were representative of the population. This household survey collects information regarding household assets, land holdings, household size, housing, expenditure pattern, age, occupation, consumption pattern, educational attainment and health status of household members. The education module includes: literacy rate, class attainment, causes of absence from school, types of school, number of recipients of stipends, number of teachers, current enrolment and annual expenditure on schooling (conditional on enrolment) are provided. Household members who belong to age groups 5 years and above are included in this module. Literacy data were collected based on the UNESCO criterion as to whether a person can „with understanding both read and write a short, simple statement on his/her everyday life‟. Information at community village level was covered by the „Household Income and Expenditure Survey (HIES), 2000‟ during the same reference period. This survey focuses on general information such as economic activities of the household, agricultural production, facilities existing at the village level, physical and social infrastructure, occurrence of natural calamities, prices of commodities and wages prevailing in the market etc. Therefore, community characteristics and infrastructure data regarding distance to schools from the village, distance to village from Thana Head Quarter, supply of electricity in the village, nearest bus station, distance to health centres and the number of teachers in the school are collected from 252 rural PSUs.
The „Child Nutrition Survey (CNS), 2000‟ was conducted simultaneously by BBS in 442 PSUs (mouza28/mahalla) in the same households as covered by HIES 2000, where at least one survey child of age 6 to71 months was available. The total number of target aged children was 4,000 out of which 2,850 were from rural areas and 1,150 were from urban areas. This is summarised in Table 5.5.
28 Mauza is the smallest unit which consists of few households at the village areas while its urban counter part is called Mahhalla.
166 Table: 5.5 Interviewed Children under CNS Survey 2000 Area Sample Households Interviewed Children (CNS) (HIES) (Age 6-71 months) Number Number Percent Rural 5040 2850 71 Urban 2400 1150 29 National 7440 4000 100 Source: The Report of Child Nutrition Survey of Bangladesh 2000, BBS, Ministry of Planning, Government of Bangladesh.
Table 5.5 shows that 4,000 children were surveyed in CNS 2000. Among the children, 29 percent was from the urban areas and 71 percent from the rural areas. The nutrition data of the surveyed children in terms HAZ and WAZ are estimated by BBS following the standard formula and reference of NCHS (National Centre for Health Statistics, USA). This standard is recommended by the World Health Organisation (WHO). For example, if the estimated HAZ of a child is less than 2 Standard Deviation of NCHS, USA standard, this means that the child is suffering from moderate stunting and if HAZ < -3SD indicates that the child is suffering from severe stunting. Underweight of a child is also measured in a similar way. However, anthropometric indicators such as stunting indicated by HAZ (low height), underweight indicated by WAZ (low weight), information on household demographic characteristics, child feeding and care practices and caregiver/mother‟s education are obtained from household level CNS data. In order to perform empirical investigation of this thesis, household characteristics from HIES were merged with nutrition data from CNS as well as with community/village level data. The models developed in this thesis for empirical investigation is solved using HIES 2000 and CNS 2000 including community survey comprising village characteristics which are unusually rich and also nationally representative for social investigation. These surveys are also relevant as they covered a wide range of household characteristics, child nutrition and community characteristics at the village level. More importantly, in the HIES 2000 framework, an education module was introduced for the first time, which offers an opportunity to investigate the educational performances in an alternative way to the standard administrative approach.
167 Administrative data such as enrolment rate, completion of primary school and literacy rate are usually provided by the Ministry of Education, Bangladesh and its attached institution Bangladesh Bureau of Education Information System (BANBEIS). These data are largely based on school registrars and sometimes deviate from the actual situation. By contrast, HIES provides data on school attendance, rate of absence, teacher-pupil ratio and expenditure on schooling based on field level information. Thus, household level survey data in Bangladesh offer an opportunity to investigate the effects of maternal education on children‟s „school attendance‟ in a quantitative framework. It explains the factors affecting school attendance to address the challenges confronted in these sectors (as discussed in Chapter 3). Similarly, the effect of maternal education on child nutrition can be investigated by using CNS data (as discussed in Chapter 4). However, household survey has some key issues: the unit of observation can be the household (a group of persons eating and living together) or the individuals within the household. In practice, some information is collected at the household level (types of house for residence, use of drinking water) and other information at the individual level (income, expenditure, education etc.). Thus, aggregation is needed to bring this data to at the household level. Other analysis such as education or health can be done at the individual level.
The choice of using HIES 2000 and CNS 2000 including community survey in this thesis to evaluate the effects of parent‟s education and particularly mother‟s education on children‟s school attendance and child nutrition was because a substantial social change occurred in Bangladesh during the 1990s. In the social sector, policies and programs were undertaken by successive governments in the 1990s that would have significant impacts on the socio-economic arenas and may provide in-depth insights for new policy prescriptions. In addition, these areas have not previously been explored rigorously.
As the most commonly used survey in Bangladesh, educational and child nutrition analysis can be undertaken using the data collected by Household Income and Expenditure Survey (HIES), a single cross section nationally representative sample conducted by the Bangladesh Bureau of Statistics (BBS) as stated early. Thus the proposed thesis based on 2000 data is expected to produce a benchmark in the
168 investigation of social issues such as children‟s school attendance as well as their nutritional status for the new century.
5.6.2 Limitation of Data There are some limitations in the HIES 2000 data set, particularly in the education data. Literacy data was collected based on UNESCO criterion which is a useful working definition, but those who can pass such a test are not necessarily functionally literate. Information of enrolment and educational attainment is provided without indicating cognitive achievement, which limits an analysis based on the children‟s real educational attainment. In defining the level of education, code-1 indicates class-I of schooling, code-2 indicates class-II and so on. Code-11 does not correspond to class-XI, rather it indicates XII class, and code-12 indicates graduate level or equivalent. It is observed that code-17 includes many boys and girls below 17 years, which is not consistent with the appropriate level of education. Moreover, missing values are exorbitantly high as emerged in manipulating the education data. These values might be either people having no formal education (although will not necessarily be illiterate) or recorded inappropriately, which is not a true missing value. Finally, no clear indication29 is provided about the meaning of the code of „0‟ as well as missing values indicated by the symbol (.) in the data set.
The World Bank has worked on various aspects of poverty and achievements in attaining MDGs in Bangladesh (as shown in Appendix Table A3.3) extensively using HIES 2000 data. According to the World Bank (2005), these missing (.) values are assumed as a person having no education. As a result, a considerable number of parents having no education are included in the data and thus data quality is reduced to some extent. There are also some mismatches with children not attending the class for their respective age e.g. a 5 years old child may attend class 3 or a 14 years old child may attend primary level, although these cases are very few in number and can easily be excluded from the data. HIES 2000 education data does not include informal education in the education category, and thus a considerable number of household members fall into the illiterate group again limiting the quality of data.
29 Code „0‟ means having no education and (.) meaning either value is missing or not recorded properly, which does not mean „no education‟
169 In HIES 2000, community survey data are collected from 252 rural PSUs (5040 households), therefore this survey produced rural information only. In the Child Nutrition Survey (CNS) 2000, there are 4,000 children who belonged to age group 6-71 months while other relevant agencies such as Helen Keller International, HKI/Bangladesh, National Institute of Population, Research and Training (NIPORT), Ministry of Health and Family Welfare consider children as belonging to age group 0-59 months. Thus, results from CNS 2000 data set need to be manipulated by making the children‟s age group 6-59 months comparable with other researchers.
This nationally representative data set is used due to its wide coverage of variables regarding household characteristics, village level data and nutrition data that have been used in previous studies as reviewed in Chapter 2. HIES 2000 data along with community characteristics and the CNS data were collected contemporaneously in 12 months reference period July 1999 to June 2000. The convenience of using this data set is that it provides information on a wide range of items simultaneously at the same reference period. The matching of the data sets at the household level is particularly useful to identify wider measures for educational attainment and nutritional development of children in the country.
Household level data have not been used by previous researchers in Bangladesh from the human capital accumulation point of view although some attempts are made on a sporadic basis. Therefore, while this intended research exercise concentrates on children‟s educational attainment and child nutrition based on household characteristics, it should provide some indications of the status of human capital accumulation in Bangladesh. HIES 2000 is considered as a benchmark for the new century being the first to measure the impact of education policies and reforms undertaken in the 1990s. Therefore, an investigation based on 2000 data would provide valuable insights into human capital accumulation during the transitional stage of development of Bangladesh 2000 onward.
Household survey provides alternative sources of education data which offers an opportunity to check the available administrative data. For example, the net primary enrolment rate provided by HIES 2000 may be lower than the official enrolment rate for
170 the same period. There are many reasons for the discrepancy between household survey- based and school administrative record-based data regarding enrolment rates and literacy rates. First, household surveys typically obtain information on whether a child is attending school at the time of the survey, while administrative data refer to students enrolled in the registers of the school at the beginning of the school year. The consideration of household level data is justified as it provides a more realistic picture of school attendance data linked to the socio-economic conditions of the household.
With some limitations, these official data are by and large nationally representative, acceptable and reliable and widely used (Ravallion & Wodon 2000; Basu, Narayan & Ravallion 2002; World Bank‟s MDG Report 2005; Iversen & Palmer-Jones 2008) for poverty analysis, analysis for consumption patterns, health and nutritional status, educational attainment and social safety net in both national and international arenas. Moreover, the Bangladesh Bureau of Educational Information and Statistics (BANBEIS) published education data under the Ministry of Education. The organization Campaign for Education (CAMPE30) also works extensively on education. The Bangladesh Bureau of Statistics (BBS) also produces education data, particularly adult literacy rate on a regular basis. Nutrition related information is collected by Helen Keller International/Bangladesh in collaboration with the Ministry of Health and Family Welfare. The National Institute of Population, Research and Training (NIPORT), Multiple Cluster Indicators regarding anthropometry, schooling and other social indicators, BBS in collaboration with UNICEF and the Sample Vital Registration System, BBS also work with nutrition data. These surveys are also reviewed to compare with the estimated results in chapter 6 and 7. However, the household survey data of Bangladesh is widely used in explaining the relationships among various socio-economic indicators in this country. This may support the use of this data set to test the likely impact of mother‟s education on children‟s school attendance and on nutritional status.
30 Campaign for Popular Education (CAMPE) is a non-governmental organization (NGO), which published report annually on education regarding different theme covering a wide range of household survey. The reports are reliable and widely accepted in the field of education.
171 5.7 Concluding Remark
In an econometric framework described in this chapter articulates the two research questions identified to be investigated. These are: i) whether mother‟s education influences significantly children‟s school attendance, and ii) whether mother‟s education improves significantly children‟s nutritional status? Two different models are specified for measuring these effects based on human capital theory (as described in Chapter 2) and in the socio-economic context of Bangladesh (as described in Chapters 3 and 4) by using household data from Bangladesh. Hypotheses in this regard are also developed in accordance with the problems to be investigated. In both cases, some common independent variables are considered although each model has specific characteristics. In measuring educational attainment, a probit model is used which fits maximum likelihood method (d-probit), because children‟s „school attendance‟ is a qualitative and dichotomous dependent variable bearing the answer „yes‟ or „no‟ in question of school attendance. In the case of child nutrition, multiple regression model is used due to quantitative nature of dependent variable. Child nutrition is measured in terms of HAZ (stunting) and WAZ (underweight) which are given as numeric values in the child nutrition data set. The ordinary least square (OLS) method is appropriate for solving these models. As reviewed in the literature and given the socio economic context of Bangladesh, in both models some common independent variables such as household income, parent‟s mother‟s individual levels of education and Sex2 are considered. Model specific independent variables such as age of children, average expenditure of schooling, distance to school, supply of electricity are included in Equation 5.9 assigned for school attendance. On the other hand, sources of drinking water, types of toilet, washing mother hands, and distance to health centres are included in Equations 5.15 assigned for child nutrition.
These models will be solved using household level data from Bangladesh. Sources of data used in this thesis are „Household Income and Expenditure Survey 2000‟ including community survey comprising village characteristics „Child Nutrition Survey 2000‟of Bangladesh. Limitations of this data set are also acknowledged which may affect the results.
172 In the next chapter (Chapter 6), a detailed description of the variables included in Equation 5.9 in relation to school attendance and the estimated results will be presented with explanation of estimated results. The chapter will also present the technical analysis regarding children‟s school attendance with major findings and their acceptability, explanation of hypothesis in detail. In Chapter 7, a detailed description of the variables included in Equation 5.15 in relation to child nutrition and the estimated results will be presented. The chapter will also present the technical analysis regarding child nutrition with major findings and their acceptability, explanation of hypothesis in detail. Integrating the results obtained from the both models in respect of children‟s school attendance and nutrition in relation to women‟s education would provide a very useful and effective set of policy recommendations to surmount the crises faced by the developing countries like Bangladesh. It would have great implications from the perspective of human capital theory.
173
Chapter 6
Educational Attainment: Data Analysis and the Expected Results
In Chapter 5, the econometric framework used in this study was explained. It introduced the hypotheses, model specification, regression techniques and data sources, which are used in empirical investigation. It is argued that children‟s school attendance is strongly positively associated with mother‟s education. Mother‟s education also plays a significant role on children‟s nutritional improvement. Benefits accumulated at the household level from an educated mother significantly contribute to promoting economic growth above any direct benefits from the mother‟s labour force participation itself. Better educated children contribute to the economic development of a country in the longer term. That chapter therefore, provided a systematic framework to investigate the arguments developed in chapter 2, within the socio-economic context of Bangladesh. The objective of this chapter is to develop the empirical evidence of the effects of maternal education on children‟s school attendance based on equation 5.9 which was constructed in chapter 5. The chapter discusses the estimated results on school attendance with regard to household income, parent‟s education, gender, expenditure on schooling and available infrastructure. It also explains the expected relationships between regressors and the dependent variable along with a discussion of the major findings obtained from the empirical analysis. The chapter contains five sections. Section one explains both dependent variables and independent variables considered in school attendance function. Section two presents the estimated results obtained from the model developed for education function by using household data from Bangladesh. Section three explains the estimated results while section four explains the hypotheses as per results obtained from the empirical investigation. Finally, section five concludes the chapter.
174 6.1 Description of Variables In the context of developing countries, the literature discussed in Chapter 2 shows that children‟s school attendance is largely influenced by household income, parent‟s education, occupation of household head, land holdings and asset base (Glewwe & Jacoby 1994; Mare & Maralani 2005; Boyle et al. 2006). Expenditure of schooling, distance to school, transport availability, presence of boundary wall, supply of drinking water and sanitation are also important factors in attaining educational achievement. The Bangladesh‟s education system discussed in Chapter 3 also shows that school enrolment largely depends on these factors. Thus, household income, parent‟s individual levels of education, female headed household, girl‟s status in school attendance, year-wise age of children, average expenditure of schooling, distance to school from the village and supply of electricity particularly to the rural households are determined as independent variables in the socio-economic context of Bangladesh (Albert 2006). Among the independent variables, distance to school and supply of electricity to the household indicate village characteristics. Both dependent variable and independent variables used in Equation 5.9 (as shown in Chapter 3) are explained in the following sections.
6.1.1 Dependent Variable I. School Attendance (S)
In investigating the effect of mother‟s education on children‟s educational attainment, „school attendance‟ is used as a dependent variable. In Bangladesh, primary school is officially defined as class I to V and the children need to belong to the age group 6-10 years for attending primary school. By exploring the data set, total number of children attended primary school is 5,651. For secondary school, the official age group is 11-15 years for attending classes VI to X. Although official age group is 11-15 years, most children in Bangladesh start primary school at older age than the official age and many take 6 to 7 years to complete the 5-year primary education cycle (discussed in chapter 3). Thus, children aged 11-15 years may not cover the actual number of secondary school aged children. To capture the maximum number of children, two more years than the official age i.e. 11-17 years are considered for secondary school attendance. The number of secondary school attended children is 6,351.
175 In estimating the effect of mother's education on children's school attendance, two separate models are estimated: one for primary school and the other for secondary school. In both cases, school attendance is estimated from national perspective as well as from rural perspective. It is worth noting that since secondary school children had to complete primary school, therefore, selection of secondary school children is not independent of primary school and thus may have selection bias. Selection bias is usually addressed by using an instrumental variable, but in this investigation, it is not possible to apply due to difficulty to find an appropriate instrumental variable (which fits 2SLS method) from the data set. However, in cross section household data, individuals in primary school and individuals in secondary school are two different groups in age cohorts and thus, the question of selection bias is not very dominant (Gujarati 1995).
There may have influences of inter-related variables such as household income and parent‟s education (Breierova & Duflo 2002) which may have great influence on children‟s school attendance. This multicollinearity problem is identified by calculating the table reporting variance inflation factors (shown in Appendix Tables A8.5 and A9.5) which show that the severity of multicollinearity is not a matter of concern either for primary school or for secondary, because the VIF value of any variable did not exceed the cut off value 10 which indicates the severity of multicollinearity.
The other kind of influence on school attendance called endogeneity bias may also exist due to unobserved family characteristics. This bias is also addressed by using an instrumental variable but again it is very difficult to find an instrumental variable from the data set. However, although, there remain some limitations to measure the individual influence of each variable on school attendance, estimation of Equation 5.9 will provide valuable findings in respect of children's educational attainment.
6.1.2 Independent Variables I. Household Income (Monthly per capita Expenditure -MPCE) Household income is one of the most powerful variables in explaining children‟s school attendance as discussed in Chapters 2 and 3. Years of children‟s schooling and its quality largely depend on household‟s income. If a household‟s income is high, children‟s educational score is also expected to be high (Behrman & Knowels 1999;
176 Boyle et al. 2006). In Bangladesh‟s Household Income and Expenditure Survey (HIES) 2000 data set, sources of income were not defined in a standard manner and most income earning sources of households were in informal categories. Hence, it is difficult to define „income‟ in a uniform way and thus the problem of measurement error may arise. By contrast, household expenditure is widely used as a proxy of income (Deaton 1997; World Bank 2005). A wealth index can also be used to measure the effects of socio- economic status of household on child mortality, literacy rate and other outcomes (NIPORT 2005; Lincove 2009). In this thesis, monthly per capita expenditure (MPCE) is used as a proxy of household income in explaining the effect of mother's education on children‟s school attendance.
MPCE is calculated based on the household‟s expenditures incurred on daily, weekly, monthly or annual purposes from HIES, 2000. Daily and weekly expenditures of households were given in the data set for 2 weeks (14 days) reference period. Thus, monthly per capita expenditure (MPCE) is calculated by [(daily and weekly expenditure * 30 / 14 + monthly expenditure + annual expenditure / 12) / household size]31. The relation between children‟s school attendance and MPCE is expected to be positive. The differential effects of MPCE on school attendance are also explored by categorising quintile MPCE. It is worth noting that the distribution of MPCE is relatively skewed (as shown in Appendix C: Figure CF1.6) and thus log MPCE (as shown in Appendix C: Figure CF2.6) is also analysed. It is found by examining the estimated coefficients from using MPCE and the coefficients from using log MPEC that the results are very similar to each other. Therefore, use of MPCE to find the effects of mother‟s education on children‟s school attendance is justified.
II. Parent’s Education [Father’s Education (FE), Mother’s Education (ME)]
Parent‟s education is a very powerful predictor for their children‟s educational attainment (Glick & Sahn 2000; Ermisch & Franchesconi 2001; Mare & Maralani 2005). In order to analyse the effects of father‟s education as well as mother‟s education
31 The information on daily and weekly expenditures for households were collected for two weeks reference period, while the households incurred some monthly expenditure as well as annual expenditure. Therefore, all expenditures expressed in a monthly expenditure.
177 separately, individual parents of children need to be sorted because parents of children are not defined in the data set.
To identify the individual parents, 14 types of given relationships32 of household members with the household head (HH) were sorted. Household members not exceeding 17 years of age is defined as a child and thus, a total number of 17,380 children are identified. Household member had to be more than 18 years to act as household head and male, which is assumed to be the father where present. Household head is defined by the code-1, which indicates male while code-2 indicates female. Categorizing by sex, individual parents of a child were identified. Thus, the number of children having both parents (father and mother) is 15,974 and this is around 92 per cent of total number of children. The relationships between household members and the head of household so identified are presented in Table 6.1.
Table 6.1: Category of Parent’s by sex (Boy and Girl) Types of Parents Total Boy Girl As (%) of total
Both parents 15974 8318 7656 91.91 Father only 586 304 282 3.37 Mother only 51 26 25 0.30 Neither parent 17 17 0 0.10 HH as Brother 343 159 184 1.97 Others 409 201 208 2.35
Total 17380 9025 (52%) 8355 (48 %) 100 Source: Author‟s calculation from HIES, 2000, BBS, Ministry of Planning, Government of Bangladesh.
Table 6.1 shows that around 92 per cent children had both father and mother while only 3.67 per cent children had a single parent either father or mother. The table also shows that around 2 percent children had a brother who was head of household. Around 2.4 per cent children belong to „others‟ category that did not have any specific relation with household head. In order to isolate the individual effect of each parent‟s education on children‟s school attendance, father and mother were sorted separately.
32 In data set, 14 types of relationships between household head and household members are given. These are: 1= Household Head, 2= Husband/Wife, 3=Son/Daughter, 4=Son in law/Daughter in law, 5= Grand son/ Grand daughter, 6= Father/Mother, 7= Brother/Sister, 8= Nephew/Niece, 9= Father in law/Mother in law 10 = Brother in law / Sister in law, 11= Other relatives, 12 = Servant, 13=Employee, 14=Others
178 In the HIES 2000 data set, educational attainment was given by 17 codes33 consisting of all education categories from class 1 to graduation and above including persons having no education. Based on this information, both father‟s and mother‟s education are categorised into five levels in accordance with the standard levels of the education system in Bangladesh (discussed in Chapter 3). These categories are: no education, primary level (class I-V), lower secondary level (class VI-VIII), higher secondary level (class IX-XII) and graduate and above (class XIII and above). Level-wise composition of parent‟s education is given in Table 6.2.
Table 6.2: Education Level of Parent’s of Children Level of education Father’s education Mother’s education Frequency (%) Frequency (%)
No education* 10151 58.4 11902 68.5 Primary level (class I – V) 2386 13.73 2411 13.87 Lower secondary (VI- VIII) 1517 8.73 1478 8.5 Higher secondary level (IX-XII) 2571 14.79 1425 8.2 Graduate and above (XIII & above) 755 4.34 160 0.92
Total 17380 100.00 17380 100.00
Note: An „*‟ indicates „no education‟ category which includes missing values.
Source: Author‟s calculation from HIES, 2000, BBS, Ministry of Planning, Dhaka.
Table 6.2 indicates that most parents belong to „no education‟ group, which also includes missing values (discussed in section 5.5.1 in Chapter 5). 58.4 per cent of children‟s fathers did not have any education while 68.5 percent children‟s mothers had no education. Noticeably, the number of parents having no education is quite large. Including the missing values, the „no education‟ category may inflate the real data and thus may influence the estimated results. However, among the parents having some education, most of them are concentrated either in primary education or in secondary education and only a few had graduation or above degree (father: 4.34%; mother: 0.92%). The Education Watch 2003-04 supports this characteristics and shows that one third of primary school children had both parents illiterate while around half of children had
33 There are 17 codes given in data set as against the survey question, „Which is the highest class did you pass? The 17 education codes are: 0 = Not passed class one, 1= class one, 2 = class two, 3 = class three, 4 = class four, 5 = class five, 6=class six, 7 = class seven, 8 = class eight, 9 = class nine, 10 = class ten/secondary level, 11 = class twelve/higher secondary level, 12 = graduation /equivalent, 13 = post graduation, 14 = Doctor/ Engineer, 15 = diploma (technical), 16 = vocational, 17 = Others.
179 mothers were illiterate (CAMPE 2005). The Bangladesh Demographic and Health Survey 2004 mentioned that a large proportion of men and women had no education in the sample of that survey (NIPORT 2005). Therefore, Household Income and Expenditure Survey 2000 data used in this thesis conforms to the other national level statistics.
The total number of children aged 0-17 years found in the sample population is 17,380. Among them, primary school attended children are 5,651 while children attended secondary school are 6,351. Children aged 0-5 years (5378) are not considered as students and the unit of observation is child.
In order to analyse the effects of parent‟s education on children‟s school attendance, the influence of associative matting needs to be addressed. Usually, an educated woman chooses an educated man and thus they have higher household income. This, in turn enhances children‟s school attendance. By using an instrumental variable, this problem could be addressed but it is very difficult to find an appropriate instrumental variable in the data set to capture this effect. In the case of father‟s and mother‟s education, level-wise education dummy variables are used (discussed in Table 5.2 in Chapter 5). School attendance is expected to respond positively with the increase of parent‟s individual levels of education and thus the expected signs would be positive.
III. Father’s Education*boy (FE*b) and Mother’s Education*girl (ME*g)
Interaction variables basically represent synergistic effects of two or more variables on the dependent variable. In order to explore the gender effect on school attendance that is the potential real relationships between father‟s education with boy child and mother‟s education with girl child, interaction variables FE*b and ME*g are considered respectively. Interaction dummy variables FE*b based on father‟s level wise education and boy child and ME*g based on mother‟s level wise education and girl child are used in this regard. These interaction variables may provide possible explanation as to whether there is a positive relationship between father‟s levels of education and boy‟s school attendance or between mother‟s levels of education and girl‟s school attendance
(H2). However, school attendance is expected to respond positively with the increase of
180 parent‟s individual levels of education in the cases of interaction. Therefore, the expected signs of interaction variables would be positive. IV. Female Headed Household (FHH)
The discussion in Chapters 3 and 4 indicated that in Bangladesh, women are mostly unable to influence the decisions undertaken at household level as well as lacking control over household resources. This difference was illustrated in a study by Helen Keller International Bangladesh (2001), which found that female headed household (FHH) even with lower income, spent more on food and medical care and made better choices regarding nutritious food than male headed household (MHH). In order to determine the differential impacts of female headed and male headed household on children‟s school attendance, variable FHH is considered. If FHH takes the value 1 and MHH takes the value 0, this indicates the influence of FHH on school attendance compared to MHH because MHH is treated as a base category. However, the relationship of FHH with dependent variable is expected to be negative and thus the sign would be
negative. V. Girls’ Status in School Attendance (Sex2) One of the major focuses of this thesis is to explore gender dimension in respect to the research questions. In order to examine the differential status whether girls‟ lagged behind boys in school attendance (primary or secondary level), the variable Sex2 is considered as an independent variable. This variable explains the girl‟s status (Sex2 = 1) in school attendance compared to boy (Sex1 = 0) because boy is considered as base category. In addition, the intuition that the presence of older girl siblings in a household leads to a higher probability of a girl‟s attending primary school was also explored. Data set was examined in order to find out the number of households which had the first child girl and older than 12 years (if older girl siblings are classified as more than 12 years old). In the same household, there must be a second child who would be a girl and also eligible for attending primary school (logically secondary school would not be considered). Among the total households, a very few households had these types of characteristics. For example, there are some households which had the second child girl, but she was not eligible for primary school as her age was less than 5 years. Again, there
181 are some households which had second child girl but she was also older than 12 years. In many cases, second child was the boy. Thus, a very few number of households had the required characteristics which could not produce any meaningful outcome. The relation of Sex2 with school attendance is expected to be negative. However, in practice, due to undertaken various policies and programs for girls‟ education in Bangladesh, particularly at primary and secondary school, the response might be positive. If girls outnumbered boys‟ at primary school or at secondary school, the expected signs would be positive or other wise it would be negative. Therefore, the signs of Sex2 may be either positive or negative.
VI. Age of Child (AC) Age of a child is an important indicator which identifies whether a child is in each class at its official age34 or whether they are delaying or repeating grades. If a child starts school at the official age and completes schooling early, he or she achieves more cognitive skills by remaining longer in educational institutions (Glewwe & Jacoby 1994). In the context of Bangladesh, many studies (World Bank 2005) used this factor as a right hand side variable because age of entry at school, year of completion of grade and repetitions of grades - have implications in educational attainment. Therefore, age of children is considered as an independent variable in determining age specific effect on children‟s school attendance. In the Household Income and Expenditure Survey 2000 data, it is observed in Bangladesh that age cohort of 6-10 years has the highest frequency of educated people followed by age cohorts 11-15 years and 26-35 years (as shown in Appendix Table A11.6). These indicate that the relatively younger cohorts are more educated than the older cohorts and higher education is still very low in Bangladesh as discussed in Chapter 3. Further, in the case of primary education, most students take 1 to 2 years longer than the official age (6-10 years) to complete 5-year primary school cycle. In order to determine the age specific effects, age dummies for each year from 6 to 10 years are considered for primary school and similarly, age dummies for each year from 11 to 17 years are considered for secondary school (as discussed in Table 5.2 in Chapter 5).
34 Officially, age group for primary school enrolment is 6-10 years while 11-15 is for secondary school enrolment.
182 However, the expected sign for each year from 7 to 10 years assigned for primary school would be positive while year 6 is considered as reference year. For secondary school, the expected sign for each year from 12 to 17 years would be positive and year 11 is the reference year. But the signs of years 11 and 12 are expected to be positive if these are included at primary school. On the other hand, the signs would be negative if years 11 and 12 are included at secondary school.
VII. Average Expenditure of Schooling (AE) There are various categories of schools in Bangladesh (as discussed in Chapter 3) although government schools are most common and more than 75 percent of students attend a government primary school (BBS 2007a). The education system typically functions with wide variation in curriculum as well as from administrative aspect. Although, enrolment rates at primary school and at secondary school have increased but the enrolment rates fall sharply at higher secondary and at tertiary levels. In Bangladesh, the burden of expenditure of schooling falls heavily on families and this particularly affects poor families. Since expenditure increases rapidly as the level of education increases and thus the drop out rates become higher at higher levels of education. The Education Watch 2001 showed, on average, parents of a primary school student spent Tk.1000 (around US$ 20) per year, which is about two percent of average household income (CAMPE 2002). The highest drop out rates are concentrated in disadvantaged people particularly rickshaw pullers, daily labourers and other vulnerable groups (BBS 2007a). Parents in Bangladesh are too poor to bear the expenditures on schooling even though tuition is free for all students irrespective of boys and girls at primary school. Taking into account this context, various support programs such as stipend program with tuition fee waiver at primary school, free distribution of text books are implemented by successive governments of Bangladesh and thus the actual expenditure of schooling may be substantially lower, which is shown in the following Table 6.3.
However, cost of schooling is an important indicator in explaining children‟s school attendance in Bangladesh. The cost of schooling includes both direct and opportunity costs (lost income or unpaid labour while staying at school). Due to data constraint regarding estimating opportunity costs, average annual expenditure is used to analyse the effect on school attendance. This average expenditure is estimated by
183 regressing household‟s annual expenditure35 on schooling given in the data set. Level- wise average expenditure is presented in Table 6.3.
Table 6.3: Level-wise Annual Schooling Expenditure
Enrolment Average expenditure in Taka (local currency)
Both Boy Girl
Primary level 231 274 182.5 L. Secondary level 3480 5605 1497 H. Secondary level 4327 4004 4700 Graduate and above 6408 5768 8596 Source: Author‟s calculation based on HIES, 2000 BBS, Ministry of Planning, Government of Bangladesh
Table 6.3 shows that average expenditure at primary school is substantially lower than those of secondary and graduate levels. At primary school, both boys and girls enjoy tuition fee waiver along with monthly stipend but the focus is on girl‟s enrolment by the ongoing „Primary School Stipend Program‟. Thus, girl‟s expenditure at primary school (Taka 182.5/-) is considerably lower than that of boy (Taka 274/-). Girls‟ expenditure in lower secondary education is again substantially lower than that of boys‟ because of the ongoing „Stipend Program for Girls‟ as discussed in Chapter 3. Due to substantial support from the government to boost up enrolment and enhance quality, household expenditure for primary school is considerably low than the actual expenditure. Therefore, the
expected sign of average expenditure may not be negative. It is important to note that average expenditure is unusually highly negatively correlated with primary school attendance (shown in Annexure B5 Correlation Table B5.1). At the same time, if this variable is included in primary school attendance function, it undermines the coefficients of other variables and thus tended to become insignificant (shown in Appendix Table: A12.6). This may be due to high variation in average expenditure between primary school and secondary school or may be due to data problem. Therefore, it is not worthwhile to include average expenditure in primary school attendance function. By contrast, secondary school attendance responds positively with
35 In HIES 2000, annual expenditure on schooling is given which includes expenditure for books, notes, tuition fees, examination fees, private tutoring, transport cost, donation and other charges.
184 average expenditure and the other coefficients are also consistent. Therefore, average expenditure of schooling is included in secondary school attendance function only.
VIII. Distance to School (DS)
[Distance to school is one of the factors which deter children‟s school attendance. Parents are usually more reluctant to send their girls relative to boys to distant schools due to safety concern. Empirical studies have shown that children‟s school attendance is significantly interrupted when schools are located in substantial distant places from home especially for girls in the rural areas (Hill & King 1993; Alderman et al.1996; Birdsall & Orivel 1996). These studies showed by using travel time to school that girls‟ education is more affected by distance to school compared to boys. To analyse the impact of distance to school in the context of Bangladesh, this variable is included in the model. The government and privately owned general schools, Madrasas and technical schools are included when school is considered in the variable „distance to school‟ (as discussed in Chapter 3). If any school is located nearby the village around 1 to 1.5 kilometre, the variable takes the value „0‟. Otherwise the distance is measured by travel time to schools. Therefore, average travel time to reach the above stated schools given in the data set is used to measure the effect of „distance to school‟ from the village on school attendance. It is expected that if the distance is long, children‟s school attendance would be low. Thus, the expected sign with dependent variable would be negative.
IX. Supply of Electricity (SE) The electricity coverage was 44 per cent at national level in 2005. The rural coverage was 31 per cent as against 83 per cent in urban areas (BBS 2007a). The proportion of total households connected to electricity was 31 per cent in 2000 with around 19 per cent rural connection as against 80.4 per cent in urban areas (BBS 2007a). This indicates a wide variation in access to electricity between rural and urban households. The supply of electricity is considered as one of the important infrastructure related components which contributes significantly to educational attainment particularly in the rural areas (World Bank 2005). Electricity connection is an important component which helps children to perform study and other related activities well at household level
185 and encourages them to attend school regularly. Thus, the expected relation of electricity connection to household with school attendance would be positive.
6.2 Estimated Results on Children’s School Attendance For school attendance, the probit model (Equation 5.9) is estimated both for the national level as well as for the rural level by using the „differential probit’ (dprobit) command through Stata. The LR chi2-distribution (maximum likelihood) measures the overall fitness of the model and provides a test of the null hypothesis (H0) that the slope coefficients are simultaneously zero. The computed value LR chi2-distribution suggests the rejection of null hypothesis, particularly at = 0.01, or in other words, this indicates a good fit of Equation 5.9. Alternatively, p-value is sufficiently low for rejecting null hypothesis (H0), which means at least one independent variable affects children‟s school attendance. For individual estimate, z-score shows the significance of the estimates and alternatively, p-value indicates the significance of individual estimates where three levels of significance: 1 per cent, 5 per cent and 10 per cent are considered. The results on primary school attendance at the national level and for the rural level are presented in the following tables. 6.2.1 Primary School Attendance: National Level
Children‟s primary school attendance is assessed by considering household income, parent‟s individual levels of education, interaction variables, female headed household, gender differential and age of child are considered. It is important to note that average expenditure of schooling is not included in primary school attendance function due to inconvenience (shown in Appendix Table A12.6 and described in section 6.1.2 VI) of estimation. However, at the national level, the number of primary school aged children is 5,651. It is observed from the Table 6.4 that LR chi squared is 897, which is large from the critical value for rejecting H0 and the corresponding p-value is also sufficiently low for rejecting H0. Primary school attendance is however, influenced by at least one independent variable i.e. the model is good for explaining primary school attendance. Estimated z-scores for individual estimates and the corresponding p-values indicate the levels of significance to draw inference. Detailed results on marginal effects and the respective z-scores from the national perspective are presented in Table 6.4.
186 Table 6.4: Primary School Attendance by Children (6-10 years): National Level Independent Variables Marginal Effects Z-score
Household Income (MPCE) .0001*** 4.86
Father’s Education (FE) No education (reference level) - - Primary level .13*** 4.59 Lower secondary .11*** 3.00 Higher secondary .11*** 3.28 Graduate & above .01 0.10
Mother’s Education (ME) No education (reference level) - - Primary level .11*** 3.89 Lower secondary .08* 1.87 Higher secondary .10** 2.05 Graduate & above -.19 -1.42
Father’s Education x boy child (FE*b) Boy * No education (reference level) - - Boy * Primary level -.01 -.34 Boy * Lower secondary -.01 -0.13 Boy * Higher secondary .02 0.46 Boy * Graduate & above .10 1.12
Mother’s Education x girl child (ME*g) Girl * No education (reference level) - - Girl * Primary level .02 0.45 Girl * Lower secondary .03 0.46 Girl * Higher secondary -.04 -0.58 Girl * Graduate & above .05 0.29
Female headed Household (FHH) - -
Girl’s Status (Sex2) in School Attendance 1.73 .03* Age of child (AC) Year 6 (reference year) - - Year 7 -.51 -20.72 Year 8 -.25 -10.55 Year 9 -.08 -3.47 Year 10 -.15 -6.24
LR chi-squared (22) = 897; Prob > chi squared = 0.0000
[H0: a2 = a3 = a4 = 0 (no linear relationship); H1: at least one independent variable affects school attendance]
Log-likelihood ratio = -3166 Pseudo R-squared = 0.124
Observed P = .66 Predicted P = .68 (at x-bar); Number of observations = 5651
Level of Significance: * p < 0.10 indicates significant at 10 % level; ** p < 0.05 indicates significant at 5 % level; and *** p < 0. 01 indicates significant at 1% level
Notes: Household Income and Expenditure Survey 2000 data merged with village level data. All coefficients are expressed as marginal effects (the change in probability of a child‟s primary school attendance with one unit change in right hand side variables). „- ` indicates found no result. Constant is not considered.
Source: Authors calculation based on HIES 2000, BBS, Ministry of Planning, Government of Bangladesh.
187 Table 6.4 shows that household income has significant positive effect on children‟s primary school attendance at the national level, although the coefficient is very nominal. This means school attendance by a child increases if household‟s income increases. In the case of parent‟s education, both father‟s and mother‟s all levels (primary, lower secondary and higher secondary education) of education except graduate and above significantly increase children‟s primary school attendance compared to parents having no education. The interaction variables show that only graduate fathers have some effects on boys‟ school attendance while in the case of mothers‟, any level of education (primary level to graduate and above) has no impact on girl‟s school attendance. It is evident by the variable Sex2 that girls are more likely to attend school compared to boys but the variable female headed household has no effect on children‟s primary school attendance. Unexpectedly, the coefficients of year wise age of primary school from year 7 to year 10 show no positive associations with primary school attendance rather indicate negative associations with school attendance. However, detail explanation is given below.
6.2.2 Primary School Attendance: Rural Level Children‟s school attendance at rural level are analysed by including additional variables distance to school from the village and supply of electricity to the households in Equation 5.9. The number of primary school aged children at the rural level is 4053 according to the data set. It is observed that LR chi squared is 670, which is large enough from the critical value for rejecting H0 and the corresponding p-value is also sufficiently low for rejecting H0. Primary school attendance is however, influenced by at least one independent variable. The detailed estimated results reported as marginal effects including z-scores for individual estimates from the rural perspective are presented in Table 6.5.
188 Table 6.5: Primary School Attendance by Children (6-10 years): Rural Area Independent Variables Marginal Effects Z-score
Household Income (MPCE) .0001*** 5.65
Father’s Education (FE) Primary level .11*** 3.27 Lower secondary .11** 2.36 Higher secondary .09** 1.94 Graduate & above -.04 -0.41
Mother’s Education (ME) Primary level .06* 1.77 Lower secondary .09* 1.82 Higher secondary .14** 2.07 Graduate & above -.36 -1.37
Father’s Education x boy child (FE*b) Boy * Primary level -.004 -0.10 Boy * Lower secondary -.02 -0.29 Boy * Higher secondary .05 0.72 Boy * Graduate & above .04 0.31
Mother’s Education x girl child (ME*g) Girl * Primary level .06 1.29 Girl * Lower secondary .09 1.16 Girl * Higher secondary -.14 -1.33 -.28 1.25 Girl * Graduate & above - - Female headed Household (FHH)
Girl’s Status (Sex2) in School Attendance .03* 1.63
Age of child (AC) Year 6 (reference year) - - Year 7 -.52 -17.79 Year 8 -.26 -9.39 Year 9 -.09 -2.99 Year 10 -.13 -4.75 Distance to School (DS) .02 0.86
Supply of Electricity (SE) .03* 1.87
LR chi-squared (25) = 670 ; Prob > chi squared = 0.0000 [H0: a2 = a3 = a4 = 0 (no linear relationship); H1: at least one independent variable affects school attendance]
Log-likelihood ratio = -2279 Pseudo R-squared = 0.13
Observed P = .65 Predicted P = .67 (at x-bar); Number of observations = 4053
Level of Significance: * p < 0.10 indicates significant at 10 % level; ** p < 0.05 indicates significant at 5 % level; and *** p < 0. 01 indicates significant at 1% level
Notes: Household Income and Expenditure Survey 2000 data merged with village level data. All coefficients are expressed as marginal effects (the change in probability of a child‟s primary school attendance with one unit change in right hand side variables). In the case of education, no education is the reference level. In the case of age year-6 is reference year. „- ` indicates found no result. Constant is not considered.
Source: Author‟s calculation based on HIES 2000, BBS, Ministry of Planning, Government of Bangladesh.
189
Table 6.5 shows almost similar results for the rural areas as obtained for the national level (as shown in Table 6.4) in the cases of household income, parent‟s education, interactions between father‟s education with boy child and mother‟s education with girl child, female headed household, girl‟s status in school attendance and year wise age. At the rural areas, supply of electricity to household has positive effect on primary school attendance at 10 percent level of significance while distance to school from the village yields no significant effect in this respect.
6.2.3 Secondary School Attendance: National Level For secondary school, results obtained from the national perspective are based on Equation 5.9 and the total number of children is 6,351. It is observed from the Table 6.6 that LR chi-squared is 2534, which is large from the critical value for rejecting H0 and the corresponding p-value is also sufficiently low for rejecting H0 which means secondary school attendance is influenced by at least one independent variable included in the model. The estimated marginal effects along with z-scores for individual variables and the level of significance indicated by the asterisks are described below in Table 6.6.
190 Table 6.6: Secondary School Attendance by Children (11-17 years): National Level
Independent Variables Marginal Effects Z-score
Household Income (MPCE) .0001*** 11.10
Father’s Education (FE) Primary level .10*** 3.16 Lower secondary .18*** 4.54 Higher secondary .24*** 6.76 Graduate & above .08 1.43
Mother’s Education (ME) Primary level .17*** 5.21 Lower secondary .16*** 3.83 Higher secondary .15*** 3.06 Graduate & above -.09 -.76
Father’s Education *boy child (FE*b) Boy * Primary level .02 0.57 Boy * Lower secondary .06 1.10 Boy * Higher secondary .08* 1.73 Boy * Graduate & above .34*** 4.05
Mother’s Education * girl child (ME*g) Girl * Primary level .04 .83 Girl * Lower secondary .07 1.17 Girl * Higher secondary -.11 -1.47 Girl * Graduate & above -.30 -3.07
Female headed Household (FHH) .01 .08
Girl’s Status in School Attendance (Sex2) .16*** 8.76
Age of Child (AC) Year 12 .06** 2.20 Year 13 .13*** 4.04 Year 14 .12*** 3.96 Year 15 .02 0.57 Year 16 .01 0.48 Year 17 -.09 -2.81
Average Expenditure of Schooling (AE) .0001*** 27.54
LR chi-squared (26) = 2534; Prob > Chi squared = 0.000 [H0: a2 = a3 = a4 = 0 (no linear relationship); H1: at least one independent variable affects school attendance]
Log-likelihood ratio = -3014 Pseudo R-squared = 0.30
Observed P = 0.40 Predicted P = 0.39 (at x-bar); Number of observations = 6351
Level of Significance: * p < 0.10 indicates significant at 10 % level; ** p < 0.05 indicates significant at 5 % level; and *** p < 0. 01indicates significant at 1% level.
Note: All coefficients are expressed as marginal effects (i.e. the change in probability of a child for secondary school attendance with one unit change in right hand side variables). Figures in bold indicate statistically significant of the marginal effects for individual estimates. Constant is not considered. In the case of parent‟s education, „no education‟ is used as reference level. While in the case of age, year 11 is the reference year.
Source: Author‟s calculation based on HIES 2000, BBS. Ministry of Planning, Government of Bangladesh
191
Table 6.6 shows that household income increases children's secondary school attendance significantly and the coefficient is again very nominal. In the case of parent‟s education (both father and mother), from primary level to higher secondary education significantly increases children‟s secondary school attendance while graduate and above education does not have any effect on the same. Interaction variables show particularly mother‟s education does not have any effect on girl‟s school attendance while father‟s with higher secondary and above education have some effects on boy‟s school attendance. The variable female headed household although has positive association with children‟s school attendance but the coefficient is not statistically significant. However, girl‟s attendance is significantly higher compared to boy particularly at secondary level. In the case of child‟s age, the coefficients show that year 12 to year 14 have significant positive effects while year 15 and above have no effects on school attendance. At this level, the response is positive with the increase of average expenditure of schooling and the estimate is also statistically significant. The status of rural children‟s secondary school attendance is presented in Table 6.7.
6.2.4 Secondary School Attendance: Rural Level For secondary school, results obtained from the rural perspective are based on the number of children 4201. It is observed from the Table 6.7 that LR chi-squared is 1659, which is large from the critical value for rejecting H0 and the corresponding p-value is also sufficiently low for rejecting H0 which means secondary school attendance is influenced by at least one independent variable included in the model. The marginal effects and the corresponding z-scores for individual variables for the rural areas are described below in Table 6.7.
192 Table 6.7: Secondary School Attendance by Children (11-17 years): Rural Area Independent Variables Marginal Effects Z-score
11.46 Household Income (MPCE) .0002***
Father’s Education (FE) Primary level .04 0.96 Lower secondary .20*** 3.81 Higher secondary .24*** 5.43 Graduate & above .17* 1.72
Mother’s Education (ME) .12*** 3.15 Primary level 0.95 Lower secondary .05 .04 0.68 Higher secondary 6.56 Graduate & above .70***
Father’s Education *boy child (FE*b) Boy * Primary level .10** 1.98 Boy * Lower secondary .09 1.38 Boy * Higher secondary .10* 1.61 Boy * Graduate & above .27** 1.96
Mother’s Education * girl child (ME*g) Girl * Primary level .10* 1.80 Girl * Lower secondary .17** 2.12 Girl * Higher secondary .02 0.18 Girl * Graduate & above -.30 -1.86
Female headed Household (FHH) .03 0.19 ] Girl’s Status in School Attendance (Sex2) .15*** 7.22
Age of Child (AC) .04 1.02 Year 12 4.07 Year 13 .15*** .19*** 4.91 Year 14 6.02 Year 15 .23*** .09** 2.50 Year 16 3.12 Year 17 .11***
Average Expenditure of Schooling (AE) .0001*** 22.74 Distance to School (DS) .01 0.26
Supply of Electricity (SE) .04** 2.62
LR chi-squared (28) = 1659; Prob > Chi squared = 0.000 [H0: a2 = a3 = a4 = 0 (no linear relationship); H1: at least one independent variable affects school attendance] Log-likelihood ratio = -1945 Pseudo R-squared = 0.30 Observed P = 0.37 Predicted P = 0.30 (at x-bar); Number of observations = 4201
Level of Significance: * p < 0.10 indicates significant at 10 % level; ** p < 0.05 indicates significant at 5 % level; and *** p < 0. 01indicates significant at 1% level.
Note: All coefficients are expressed as marginal effects (i.e. the change in probability of a child for secondary school attendance with one unit change in right hand side variables). Figures in bold indicate statistically significant of the marginal effects for individual estimates. Constant is not considered. In the case of parent‟s education, „no education‟ is the reference level and in the case of age, year 11 is the reference year.
Source: Author‟s calculation based on HIES 2000, BBS, Ministry of Planning, Government of Bangladesh.
193 Table 6.7 shows as usual that household income increases children's secondary school attendance significantly, keeping all other factors constant. All levels of father‟s education except primary education significantly increase children‟s secondary school attendance. While in the case of mother‟s education, primary education and graduate and above education have positive effects on school attendance while lower secondary or higher secondary education does not have any effect in this respect. At rural level, almost all levels of father‟s education have significant effects on boy‟s secondary school attendance although the levels of significance range from 5 to10 per cent. Interestingly, mothers‟ education has some effects on girls‟ school attendance at secondary level. At this level, female headed household has some positive effects on children‟s school attendance but the coefficient is not statistically significant. Similarly, girl‟s attendance is significantly higher compared to boy. The coefficients of year wise age from year13 to year 17 have significant positive effects on rural children‟s school attendance but year 12 has no impact on school attendance. However, secondary school attendance responds positively as average expenditure of schooling increases and the estimate is statistically significant. The variable „supply of electricity‟ to household has significant effect on rural children‟s school attendance while the other variable „distance to school‟ has no effect in this regard. The detailed explanation is given in the following section.
6.3 Explanation of Estimated Results of Independent variables 6.3.1 Effects of Household Income I. Primary School Attendance It is observed from the estimated results (as shown in Table 6.4) at national level that the association between household income and children‟s primary school attendance is positive and statistically significant at 1% level, although the magnitude is very nominal (.0001). In view of this relationship, it is stated that primary school attendance increases as income increases holding all other factors constant. The rural level estimated results are shown in the Table 6.5. It is important to note that apparently, no significant difference between the national level and the rural areas is observed at primary school attendance. In addition, the effects of different levels of household income are also
194 examined by categorising the household income into quintiles. Quintile wise estimates of income effects on primary school attendance are given in Table 6.8.
Table 6.8: Household Income and Primary School Attendance Income Quintile Coefficient Standard Errors
Poorest (reference group) - - Poorer .09*** .017 Middle income group .14*** .016 Richer .18*** .016 Richest .22*** .015
LR chi2(4) = 199.5 Prob > chi2 = 0.0000 Pseudo R2 = 0.028 Observed P = .66 ; Predicted P = .67 (at x-bar); Number of children = 5651
* p < 0.10; significant at 10 % level; ** p < 0.05; significant at 5 % level *** p < 0. 01; significant at 1% level
Source: Authors calculation based on HIES 2000, BBS, M/Planning, Government of Bangladesh
Table 6.8 shows that children from the poorer group respond significantly high in attending primary school compared to children from the poorest group (bottom quintile) and the response is consistently higher as the level of income increases. Results are also significant and consistent if the reference group is changed as middle income group (as shown in Appendix Table A13.6).
II. Secondary School Attendance Children‟s secondary school attendance at national level is significantly positively associated with household income at 1% level, holding all other factors constant (as shown in Table 6.6). The coefficient of secondary school attendance is again very nominal. Apparently, the coefficients of primary and secondary school attendance at the national level are almost similar. Although, in practice, household income influences higher level educational attainment more than the primary level but it may not be supported by this study. However, the rural status in secondary school attendance is shown in Table 6.7. At the rural level, the distinguished features are rural children are more likely to be older than those of national level in attending secondary school and also rural mothers have some influences on girls‟ school attendance. However, the behaviour of school attendance due to quintile wise change in income level is shown in Table 6.9.
195 Table 6.9: Household Income and School Attendance: Secondary Level Income Quintile Coefficient Standard Errors
Poorest (reference group) - - Poorer .11*** .026 Middle income group .21*** .024 Richer .35*** .022 Richest .49*** .019
LR chi2 (4) = 213 Prob > chi2 = 0.0000 Pseudo R2 = 0.46 Obs. P = .40 Pred. P = .39 (at x-bar); Number of children = 6351 * p < 0.10; significant at 10 % level ; ** p < 0.05; significant at 5 % level *** p < 0. 01; significant at 1% level Source: Authors calculation based on HIES 2000, BBS, M/Planning, Government of Bangladesh.
Table 6.9 shows that children‟s secondary school attendance significantly increases due to increase in income level compared to children from the poorest group (bottom quintile) and the responses are consistently higher from poorer group to richest group. If the reference income group is changed, children‟s response to school attendance is also significant and consistent (as shown in Appendix Table A14.7).
These results are supported by the findings on enrolment rates in Bangladesh as mentioned in the report of Household Income and Expenditure Survey 2000 (BBS 2007a). It was stated that enrolment rate for children in poor income group aged 6-10 years (primary school) was 65.7 per cent and the rate was 82.2 per cent for non-poor group. The enrolment rates were 57.9 per cent and 71.7 percent for age group 11-15 years (secondary school) for poor and non-poor groups respectively.
However, although the analysis shows that household income increases children‟s primary and secondary school attendance significantly but it can not be said that household income has stronger influence on secondary school attendance.
6.3.2 Effects of Parent’s Education
I. Primary School Attendance The analysis shows that fathers and mothers with all levels of education from primary to higher secondary education except graduation and above have significant influences on children‟s primary school attendance compared to parent‟s having no education. It is important to note that a considerable number of parents (presented in
196 section 6.1.2 II, Table 6.1) had no education in the sample population of HIES 2000 and these parents belonged to the „no education‟ group and thus „no education‟ group is comparatively large. Moreover, among the parents having some education, mostly concentrated on primary or on secondary education, only a very few had graduation and above levels of education. It is also commonly perceived that parent‟s higher education have large impact on children‟s educational attainment, but unusually, in this particular data set, parents with graduation and above levels of education have no effects on primary school attendance. This counter-intuitive result might be explained by the small number of parents (fathers: 4.36%, mothers: 0.94%) having graduate and above levels of education which is not representative to produce any impact on the same. The interaction36 variable either father‟s education and boy child (FE*b) or mother‟s education and girl child (ME*g) virtually has no effect on children‟s primary school attendance (as shown in Table 6.4). This, however, refutes the proposition that father‟s education has greater influence on boy‟s school attendance or mother‟s education has greater influence on girl‟s attendance at primary school.
II. Secondary School Attendance At secondary level, children‟s school attendance increases significantly with the increase of father‟s and mother‟s all levels of education except graduate and above compared to parent‟s having no education keeping all other factors constant. Unusually, parents with graduate and above levels of education have no effects on children‟s school attendance. This counter intuitive response may be due to a very few parents had graduate or above levels of education. For interaction between boy child and the father with higher secondary education has some effect on school attendance although the significance level is 10 per cent. Similarly, fathers with graduate or above levels of education significantly increase boys‟ school attendance compared to fathers having no education. By contrast, interaction between mother‟s levels of education with girl child produces no effects on girl‟s school attendance compared to mother‟s having no
36 Interaction variables created by interacting father‟s level of education with boy child and similarly mother‟s level of education with girl child.
197 education. This however, does not support the assertion that educated mothers motivate girls to attend more at secondary school. Since all interaction terms of father‟s education with boy child (FE*boy) and mother‟s education with girl child (ME*girl) are all insignificant, an F-test for the collective insignificance of these interaction terms is done (as shown in Appendix Tables A15.6). The obtained value of R squared for the model considering all interaction terms is 0.024, which is relatively low. The LR Chi squared is 170, which is large enough to accept that the model is good to explain children‟s primary school attendance if only interaction terms are considered only. The interaction terms indicate that all levels of father‟s education from primary to graduate and above level significantly increase boy‟s school attendance and similarly, all levels of mother‟s education significantly motivates girl‟s to attend school more. Again, the model is re-estimated by dropping these eight interaction terms for primary school attendance (as shown in Table A16.6). The obtained value of R-squared is 0.124 and corresponding LR Chi squared is 893. The resultant F- ratio is 83, which is sufficiently large enough to accept the results. However, the estimated coefficients in this alternative specification of model show that school attendance responds consistently with all variables included in the model.
From the analysis, it is worth noting to mention a number of points regarding parent‟s education and children‟s educational attainment in the following:
Fathers, usually, have more education than mothers and apparently, the influences of fathers‟ education on children‟s school attendance both at primary and secondary levels seems to higher as indicated by the respective coefficients; Father‟s and mother‟s education levels are positively correlated. This indicates that if father of a child is educated, its mother is also educated. As a result, children‟s school attendance is jointly influenced by both father‟s and mother‟s education; For both parent‟s, secondary education is more effective to contribute their children‟s educational attainment;
198 Girls are more likely to be enrolled both in primary school and in secondary school than boys, which may be due to the ongoing Stipend Program for both boy and girl at primary level and „Female Secondary School Stipend Program- FSSS‟ particularly for girls at secondary level. According to the World Bank (2001b), a national scholarship program for girls with particular focus on rural Bangladesh increased girls‟ enrolment rates even after controlling other measurable influences (Glewwe & Kremer 2005). This supports the national level statistics (discussed in chapter 3) that gender parity has already been achieved at primary level (in some cases girls exceed boys). Gender parity is closing at secondary level (World Bank, 2001b).
The analysis of parent‟s education shows both father‟s and mother‟s individual levels of education are equally important for children‟s school attendance both at primary and secondary school. However, although, mother‟s education influences children‟s school attendance significantly as hypothesised in H1 (as shown in Chapter 5), it is not supported sufficiently by this data set. Thus, this empirical investigation does not identify a unique role for educated mothers relative to fathers in determining children‟s school attendance.
6.3.3 Effect of Female Headed Household At primary school attendance, female headed household does not have any effect as the variable FHH produces no result at all at both at the national and the rural level (as shown in Tables 6.4 and 6.5). While at secondary school attendance, female headed household has some positive effect (.01) at the national level but the coefficient is not statistically significant (as shown in Table 6.6). The possible explanation may be due to the number of female headed household is not representative enough to produce any significant effect on children‟s school attendance. Similarly, although the effect is positive, the coefficient is not statistically significant indicating no distinct feature is observed due to female headed household at the rural level. However, a positive association between household income (using log MPCE) and the education of household head is observed and this relationship is shown in the following Figure 6.1.
199 Figure 6.1: MPCE (log) and Education of Household Head
Distribution of (log) Monthly per Capita Expenditure, by Household Head Education
male female
12
10
8
log(mpce)
6 4
0 1 2 3 4 0 1 2 3 4 source: author's calculations from HIES 2000/1
Figure 6.1 demonstrates that household income increases as the level37 of education of household head increases irrespective of male and female. Male household heads on average received more education than female household heads. Moreover, male headed households have education up to graduation level while female headed households ended up to below graduation level indicating no female headed household have graduate level of education or may be the number is very insignificant. However, the implication is that income is expected to be higher if household head received a higher level of education irrespective of male or female. Thus, there may an implicit effect on school attendance of parent‟s education, i.e. higher household head‟s education means higher household income, which is positively associated with children‟s school attendance (as discussed in section 6.3.1).
6.3.4 Effect of Girl’s Status in School Attendance The status of girl‟s school attendance indicated by the variable Sex2 shows that girl attends school more at primary school compared to boy controlling the effects of other variables. The coefficients are significant at 10 per cent level both at the national and at the rural level. This result is comparable with the findings of World Bank‟s MDG
37 The levels of education are indicated by: 0 = no education; 1 = primary level; 2 = lower secondary; 3 = higher secondary and 5 = graduate and above.
200 Report 2005, which showed girls were significantly more likely to be enrolled in primary school than boys. Using the same dataset HIES 2000, this report indicated that the variables such as household living standards, presence of food for education, which encourages children particularly from poor household to attend school and the female stipend program in villages are strongly positively associated with girls‟ net primary school enrolment (World Bank 2005). At secondary school, girl‟s attendance is significantly high compared to boy at 1 percent level of significance. It is also noted that apparently the coefficient of secondary school is larger than the coefficient of primary school attendance. This behaviour of girls‟ school attendance is due to implementation of stipend program and other incentives to encourage them to be attached more with secondary schools at a higher rate to catch previous lag. Thus, these programs need to be continued in order to sustain the success in enrolment already achieved in primary school.
6.3.5 Effect of Year-wise Age of Children At primary school, unexpectedly children‟s year-wise ages from 7 to 10 years have negative associations with school attendance compared to year 6 both at the national level and also for the rural areas (as shown in Table 6.4 and 6.5). No specific reason for these negative associations occurred at primary school attendance was known. At secondary school, ages from 12 to 14 years have significant positive effects on attendance while year 15 to year 17 do not have any effect as these are compared to year 11 particularly at the national level (as shown in Table 6.6). But in the rural areas, ages from 13 to 17 years have significant positive associations with secondary school attendance except year 12 (as shown in Table 6.7). This indicates that rural children start secondary school late from 13 years and also take longer years than the official age 11-15 years to complete secondary school. In exploring the question whether the children can complete primary education within the official age (6-10 years) or not - it is observed that year 11 has a significant positive association with children‟s primary school attendance which means most children take 6 years to complete primary level instead of 5 years. But in the case of year 12, the results are mixed meaning some children aged 12 years are involved with primary school and some of them are in secondary school. These results are supported by the
201 findings of Education Watch (2001) which showed most students in Bangladesh take on average 6.6 years to complete the five-year primary education cycle (CAMPE 2002). The World Bank‟s MDG Report 2005 also showed completion of primary level schooling by a child aged 12 years was significant, meaning many children take 7 years to complete primary school in Bangladesh (World Bank 2005). Therefore, Bangladesh‟s education system appears not to be very efficient as most primary school students take the 6 to 7 years to complete 5-year cycle of primary education. However, some important points regarding children‟s age of schooling are as:
Official age (6-10 years) for primary level does not represent all students of this level; Most students take 1-2 years more to complete 5 year primary school cycle; Enrolment in secondary school starts late from year 12 to year 13 and particularly rural children take longer years than the official age (11-15) years to complete secondary school.
6.3.6 Effect of Average Expenditure of Schooling Although expenditure on schooling is an important factor in educational attainment particularly for the poor families in Bangladesh, the empirical analysis did not reflect a specific reality faced by the poor households. The correlation Table: B5.1 in Annexure B5 showed that children‟s primary school attendance was unusually highly negatively correlated with average expenditure of schooling. But interestingly, when this variable (average expenditure) was included in primary school attendance function, the coefficients of other variables were undermined and tended to be insignificant (shown in Appendix Table: A12.6). This might be due to implementation of a number of education policies and programs to support the poor families for sending their children to school by which the actual expenditure of schooling is substantially low (shown in Table 6.3) or may be due to data problem. For this reason, the variable average expenditure on schooling (AE) is not included in primary school attendance function. On the other hand, this variable is included in the secondary school attendance function. The estimates for secondary school attendance show that average expenditure on schooling both at the national and the rural level are significantly positively associated
202 with children‟s school attendance, keeping all other effects constant (as shown in Tables 6.6 and 6.7). This positive association between average expenditure and school attendance may be interpreted as the impact of quality education which incurs higher expenditure and thus may influence greater school attendance. The Education Watch Report (2008) showed that rising of education expenditure such as expenditure for stationeries and private tutor‟s fees is a serious concern particularly for the poor families in continuing study even though tuition itself is free. However, the estimated result does not support that higher school expenditure reduces the probability of children‟s secondary school attendance by this data set.
6.3.7 Effect of Distance to School from the Village
It is observed from the regression analysis that „distance to school (DS)‟ from the village has positive association with primary school attendance rather than negative association. But this positive association is not significant. Similarly, this is also the case for secondary school attendance. However, this result is not supported by the findings of World Bank‟s MDG Report (2005) which showed that the longer the distance to school from the village, the lower the overall basic skill achievement for both boys and girls in Bangladesh. The possible explanation of not being the estimated result expected for primary school attendance may be the availability of government primary schools, registered non-government primary schools and Ebtedayee Madrasas (primary level) all over Bangladesh. Although secondary schools are placed comparatively distant locations but the positive association with secondary school attendance is not expected. This counter intuitive result, however, could not be explained by any particular reason.
6.3.8 Effect of Supply of Electricity to the Rural Household At the rural level, a significant positive association is observed between electricity connection to household and children‟s school attendance. At primary school attendance, the coefficient (.03) is positive and significant at 10 per cent level while at secondary level, the coefficient (.04) is statistically significant at 5 per cent level. Therefore, electricity connection to rural households has significant influence on children‟s school attendance which is evident by this data set. The World Bank‟s MDG Report (2005) using the same data set also mentioned that the supply of electricity particularly to the
203 rural household increased children‟s educational attainment in Bangladesh. Although, lack of electricity connection may not directly hamper children‟s school attendance, it may affect their ability to perform academic activities particularly at night to improve the quality of their education and thus encourage them to attend school more.
6.4 Explanation of Hypothesis The analysis provides important insights on children‟s educational attainment and its determinant which has great significance from the human capital theory. In relation to major findings, hypotheses H1 and H2 (as shown in Chapter 5) are explained as follows:
H1: mother’s education significantly influences children’s school attendance
The analysis shows that both father‟s and mother‟s all levels of education except graduation and above increase children‟s primary school attendance significantly particularly at the national level. In the case of mother‟s eduction, the levels of significance are different. For example, mothers having primary education increase children‟s school attendance significantly at 1 per cent level but the effect is significant at 10 per cent level when mothers having lower secondary education and so on (as shown in Table 6.4). Similarly, at secondary school, both parent‟s (father and mother) all levels of education except graduate and above increase significantly children‟s school attendance at 1 per cent level particularly at the national level (as shown in Table 6.6). Although apparently, it seems the coefficients of father‟s education on school attendance are higher than those of mother‟s education but it is not sure whether father‟s education influences children‟s school attendance more than mother‟s education do. However, although household income and various programs for promoting school enrolment undertaken by the government also contribute greatly to increase student‟s school attendance, the results obtained from this analysis indicate that father‟s education is equally important as mother‟s education in enhancing children‟s school attendance at both primary and secondary school but not support a unique role played by the educated mothers in this regard.
204 The other hypothesis H2 is explained as follows:
H2: there is a positive relationship between father’s level of education and boy’s school attendance and between mother’s level of education and girl’s school attendance
In exploring the above relationship, the interaction variable either father‟s education with boy child or mother‟s education with girl child has no effect on children‟s primary school attendance. At secondary level, although father‟s education (higher secondary and above) has some effects on boy‟s school attendance but mother‟s education has no impact on girl‟s attendance. As indicated in the data set that fathers are generally more educated than mothers and thus father‟s education may have some impacts on boy‟s school attendance. Therefore, H2 is supported partly for having a positive relationship between father‟s education and boy‟s school attendance but it is not supported for mother‟s education because mother‟s education does not have any effect on girl‟s school attendance. On the other hand, the positive association indicated by the variable Sex2 shows that girls outnumbered boys at both primary and secondary school after controlling all other effects. This seems to be related to the implementation of government policies and programs to overcome the cultural bias against educating girls in low income families rather than the effect of mother‟s education as hypothesised in developing the research questions. The exception to this not being happened even mothers have graduate or above levels of education.
6.5 Concluding Remarks Children‟s primary and secondary school attendance is assessed mainly by household income, parent‟s individual level of education with special focus on mother‟s education and gender differential including village characteristics. The analysis shows that children‟s school attendance is significantly increased by household income and the response increases sharply as the level of income increases. Importantly, both father‟s and mother‟s individual levels of education significantly contribute in enhancing children‟s educational attainment particularly parents with secondary education have stronger effects in this respect.
205 As observed from the estimated results based on Equation 5.9, H1 is supported to some extent that children of educated mothers are more likely to attend school than those mothers having no education but the differential effect of mother‟s education compared with father‟s education was not strong. The effect of educated mother was not as strong as it was expected which is may be due to correlation between higher income household and the educated parents. There is a tendency for educated women to marry educated men which makes it difficult to disassociate the impact of women‟s education on these outcomes despite the use of statistical techniques designed to achieve this. In addition, education of household head and government policies and programs to provide financial support for girls‟ education are also important determinant for school attendance. That is why, no disparity between boys and girls is observed in school attendance either at primary school or at secondary school rather the situation is reversed in many cases, where girls attend school more than boys.
This analysis provides some supports to the human capital approach that educating women results in higher levels of school attendance for their children, but for girls‟ the results are not sufficiently strong. Keeping this in view, this thesis explores the hypothesis that educating women has also higher impact on children‟s capacity through influencing children‟s health by contributing on nutritional improvement in the next chapter. Therefore, the second argument (H3 and H4) which is developed on the assumption that mother‟s education impacts significantly on children‟s health and nutrition by the role played at household level and thus this would enhance economic wellbeing.
In next chapter (Chapter 7), the relation between mother‟s education and child nutrition will be explained through empirical investigation which is performed based on model developed for child nutrition in respect of stunting and underweight in equation 5.15 (as shown in Chapter 5) using household level data from Bangladesh. The logic of variables used in these equations, expected relations of independent variables with dependent variables as well as their estimated results will be discussed in the following chapter.
206
Chapter 7
Child Nutrition: Data Analysis and the Expected Results
In chapter 6, the estimated results on children‟s school attendance were analysed. School attendance is significantly positively associated with household income controlling after all other effects. Both father‟s and mother‟s individual levels of education have significant effects on children‟s school attendance. Parents with secondary education have stronger effects compared to primary education. Interaction variables indicating father‟s education with boy child and mother‟s education with girl child had virtually no impact on children‟s primary school attendance, however there are some effects on secondary school. The argument that mother‟s education significantly enhances children‟s school attendance is supported by this empirical investigation but does not identify a unique role for educated mothers relative to fathers in determining school attendance. Child malnutrition is significant in Bangladesh and a large number of children die every year due to malnutrition related diseases while many survive with different degrees of malnutrition viz. moderate malnutrition and severe malnutrition (as discussed in Chapter 4). This suggests that children in Bangladesh suffer from short-term acute food deficits as reflected in low weight-for-age ratios and they also suffer from longer term chronic under-nutrition which is manifested in low height-for-age ratios. The objective of this chapter is to explain the variables included in Equation 5.15 regarding child nutrition developed in Chapter 5 and analyse the estimated results obtained from this model. The chapter also analyses the individual significance of factors contributing to improvement of child malnutrition using the „Child Nutrition Survey
(CNS) 2000‟ from Bangladesh. Hypothesis H3 is formulated based on the assumption that
207 mother‟s education significantly improves child nutrition while H4 is developed on the argument whether girls‟ nutritional status significantly differs from that of boys in terms of calorie consumption per day at household level. Hypotheses are examined based on estimated t-scores from regression results. The organisation of this chapter is as follows. Section one explains dependent variables, explanatory variables and their expected relationships. Section two describes the estimated results on child nutrition derived from the models. Section three explains the expected results from a national perspective while the results from a rural perspective are explained in section four. Section five explains the hypothesis and finally section six concludes the chapter.
7.1 Description of Variables
In accordance with the literature review and in the context of child nutrition in Bangladesh (as discussed in Chapter 4), explanatory variables are considered for Equation 5.15 (as shown in Chapter 5). These variables are: daily per capita calorie consumption, father‟s and mother‟s individual levels of education, girl‟s nutritional status compared to boy, sources of drinking water, types of toilet used by the household members, washing mother‟s hand after defecation and distance to health centres from the village. It is important to note that the variable „distance to health centre‟ indicates village characteristics, which was taken from the community survey data. On the other hand, in the child nutrition model, HAZ (height-for-age z-score) and WAZ (weight-for-age z- score) are used as dependent variables. These are described as follows.
7.1.1 Dependent Variables
I. Child Nutrition [Expressed in terms of stunting and underweight]
Child nutrition is measured by the status as to whether a child is either stunted or underweight by their respective z-scores of height (HAZ) and weight (WAZ). Basically, z- score is the deviation of the value for an individual from the median value of the reference population divided by the standard deviation for the reference. This is based on child nutrition survey data which follows the National Centre for Health Statistics
208 (NCHS, USA 1975) standard38. The scores of HAZ for stunting and WAZ for underweight are calculated by the Bangladesh Bureau of Statistics using the following formula (BBS, 2002, p74):
Z-score = [(Observed Value - Median of Reference)/Standard Deviation of Reference]
Therefore, a child is moderately stunted if his/her HAZ is less than 2 standard deviation (SD) i.e. HAZ<-2SD and a child is severely stunted if his/her HAZ is less than 3 standard deviation (or HAZ <-3SD). Similar to HAZ, WAZ is also calculated following the above formula where a child is moderately underweight if his/her WAZ is less than 2SD or WAZ <-2SD and severe underweight is defined as a child‟s WAZ being less than 3SD (or WAZ <-3SD). Therefore, the standard cut-offs39 for malnutrition using these parameters are <- 2SD for moderate and <-3SD for severe malnutrition. Usually, Bangladeshi children have lower height and weight than the international standards and thus, HAZ (height-for-age) and WAZ (weight-for-age) are in most cases negative. However, according to the Child Nutrition Survey 2000 data set, the average HAZ for children is -1.92 cm and the average WAZ is -1.97 kg indicating Bangladeshi children‟s HAZ and WAZ are normally lower than the international standards. In fact, stunting indicates a long term characteristic in explaining malnutrition while underweight indicates a short term effect on child health. If a child suffers from malnutrition for long, the probability of being stunted will be higher and it cannot be recovered. But if a child suffers from underweight, it could be improved by providing proper nursing and nutritious food. However, in order to capture both long and short term effects on child nutrition, HAZ and WAZ are considered as dependent variables in this thesis. These scores (HAZ and WAZ) are provided by the child nutrition survey 2000 data
38 The National Center for Health Statistics (NCHS), (USA)/WHO, USA standard has been used as an international reference standard in calculating height-for-age z-score (HAZ) and weight-for-age z-score (WAZ). This was formulated in 1975 by the NCHS/Center for Disease Control in the USA by combining growth data from four US sources. If HAZ is less than 2 standard deviation (HAZ <-2SD) meaning moderate stunting while HAZ <-3SD indicates severe stunting. Similarly, WAZ <- 2SD and WAZ < -3SD indicate moderate and severe underweight respectively. In 1978, WHO adopted this NCHS/CDC dataset as the reference standard in children‟s anthropometry which is also known as the NCHS/WHO reference.
39 These cut-offs are in keeping with WHO recommendations and will allow for comparisons with other large national and international datasets.
209 set and are widely used as a measure of estimating the nutritional status of children (Barrera 1991; Glewwe 1999; Boyle et al. 2006; GoB 2009b).
7.1.2 Independent Variables
I. Daily per capita Calorie Consumption (DCC)
It is well known that food is consumed essentially by every individual for survival. Every food item has its own calorie, protein and other nutrients, which are essential for healthy living and thus people need to consume food items which provide a combination of balanced calorie, protein and other micro nutrients. In Bangladesh, a large segment of the population fails to consume sufficient food items to achieve this required level of nutrition (BBS 2007a). Besides, the way in which supplementary foods are introduced to a young child is very critical, and is often particularly affected by a mother‟s lack of knowledge regarding nutritious food. Poor households are not used to providing separate food items to their children. Children, around one year of age, begin to eat the same foods as adults in the household. Thus, distribution of household food intake is likely to disadvantage young children (HKI Bangladesh 1998). Intra-household food distribution is, therefore, an important indicator and it is necessary to ensure appropriate daily calorie consumption for each household member particularly for young children. It is observed from the child nutrition survey data (HKI Bangladesh 1998; HIES 2005; NIPORT 2005, 2008) that although the average calorie intake has improved during the last few years, the nutritional gain has been comparatively low and stagnant since 2000. Helen Keller International, Bangladesh (1998) estimated that, on average, the per child daily food grain (rice and wheat) intake was 1520 calorie which contains less protein than the amount required for physical growth. This is also supported by HIES 2005 in estimating the incidence of poverty, based on daily calorie consumption per person (BBS 2007a). The World Bank‟s MDG Report (2005) mentioned that average daily calorie intake per person among the bottom two quintiles appeared inadequate relative to recommended allowances. It was also reported that Bangladesh has the slowest growth of calorie availability in South Asia. Calorie consumption per day is, therefore a vital indicator for children‟s nutritional status.
210 It is worthwhile to note that although, household income is an important variable for child nutrition, it does not necessarily ensure child‟s daily nutritional requirement in Bangladesh. In the child nutrition model, income was initially included as an explanatory variable and the effect of income on child nutrition (being stunted or underweight) was significant. But when daily per capita calorie consumption was included as a variable, the coefficient became smaller and insignificant. In this context, to have a significant effect of food intake on nutrition, daily per capita calorie consumption (DCC) was used as one of the most useful explanatory variable in this thesis Thus, daily calorie intake by the household members works as a proxy for income in explaining child nutrition. Importantly, the variable daily calorie consumption indicates a compulsion of calorie consumption particularly by the children which may not be necessarily ensured by the household income. However, HAZ and WAZ are positively associated with daily per capita calorie consumption and thereby the signs are expected to be positive.
II. Parent’s Education [Father’s Education-(FE) and Mother’s Eduction-(ME)]
As discussed in Chapter 4, parent‟s education particularly that of mother‟s plays a significant role in improving child nutrition and thus parent‟s education is closely related with child nutrition (Jahan & Hossain 1998). Knowledge about health and hygiene and its practice at home by educated mothers is a substitute for many expensive health interventions (Barrera 1990; Gannicott & Avalos 1994; Muhuri 1995). During the growing period of children, many critical decisions need to be taken by the parents, which are supported by knowledge and experiences. Hence, parent‟s particularly mother‟s of a child essentially needs some threshold level of education to maintain his/her health and nutritional requirements. The same categories of parent‟s education as used in Chapter 6 are also used in the model assigned for nutrition (Equation 5.15). In order to identify the effects of mother‟s education on child nutrition, parent‟s education was categorised into five levels which are: no education, primary education, lower secondary level, higher secondary level and graduate and above, according to the existing levels of education in Bangladesh (as discussed in Chapter 3 and in Chapter 6, section 6.1.2 II).
211 The effect of caregiver‟s education on child nutrition was also explored, but it was found in the data set that 97 percent of children‟s caregivers are their mother. Virtually, the number of caregivers other than mothers was very few and not representative enough to produce any effect on children. Thus, inclusion of this variable in the model would not be worthwhile. However, higher levels of parent‟s education are assumed to be associated with lower mortality risks for children due to having better information and knowledge on health and nutrition (Glewwe 1999). Therefore, the expected signs of parent‟s individual levels of education would be positive.
III. Girl’s Status in Child Nutrition compared to Boy (Sex2) One of the major focuses of this thesis is to explore gender dimensions in child nutrition both in terms of stunting and underweight. Similar to Chapter 6, Sex2 shows the girl‟s status in HAZ (stunting) and WAZ (underweight) compared to boy. If the variable Sex2 takes the value 1, it indicates girl and when Sex2 takes the value 0, it indicates boy and thus boy is used as the base category. The hypothesis H4 is developed as to whether any difference persists between boy and girl in daily calorie consumption, which leads to girl to be more malnourished (section 5.3 in Chapter 5). By exploring the data, it was observed that the boy on average consumed 2071 calories per day as against 2059 calories for girl. However, the sign of the variable Sex2 is expected to be negative. If Sex2 is negative, this indicates girls are malnourished compared to boys either being stunted or underweight.
IV. Sources of Drinking Water (Dr) The prevalence of malnutrition depends on the health status of household members. Both stunting and underweight are associated more with those children who had recent diarrhoea, measles or an attack of severe cough (BBS 2002b). The children who had a severe cough had the highest rate of malnutrition and the contamination caused by unsafe drinking water and lack of sanitation are also important causes of diarrheal and other infectious diseases. These infections, when they affect a child repeatedly, can cause him or her to become malnourished (BBS 2002b; NIPORT 2005). Therefore, sources of drinking water are expected to have a direct bearing on child nutrition as water born diseases seriously affect child‟s health.
212 In the data set, available water sources are given as: i) shallow tube-well, ii) deep tube-well, iii) tap water, iv) water from well40, and v) pond or river water. Water from these sources is used for drinking, bathing children and also for washing utensils. The effect of individual drinking water sources on child nutrition is investigated in this thesis. The effects of safe and non-safe drinking water sources are also explored. Shallow tube- well, deep tube-well, tap water and water from well are known as safe sources of water while non-safe water sources consist of pond water and river water. It is observed from the data that more than 90 per cent of the sample population used safe water for drinking purposes. It is noted that arsenic contamination was not considered in the data set. However, safe water is expected to have positive association with child nutrition and expected to show a positive sign. On the other hand, non-safe water is expected to have negative influence on child nutrition and thus the expected sign would be negative.
V. Types of Toilet used by the Household Members (TT)
Contamination caused by the use of unhygienic toilets particularly in the rural areas threatens public health, particularly child health and nutrition. Toilets used by the household members in the data set are given as: i) flush latrine/sanitary/water sealed, ii) pit latrine (excreta disposed under the ground), iii) pit latrine (excreta not disposed under the ground), ix) fixed-kucha41 (excreta not well managed), v) hanging latrine beside water, and vi) latrine in open space (not fixed). The effect of using individual type of toilet is examined in this study. Flush latrines/sanitary/water sealed and pit latrine (excreta disposed under the ground) are usually considered as hygienic toilets while pit latrine (excreta not disposed underground), fixed-kucha (excreta not well managed), hanging latrine beside water and latrine in open space are considered as non-hygienic toilets. A considerable proportion (around 63 per cent) of the sample population used non-hygienic types of toilet. However, the use of hygienic toilet is expected to have a positive association with child nutrition and thus the sign would be positive. By contrast, the use of non-hygienic toilet is expected to have negative influence on child nutrition and thus, the sign would be negative.
40 Well is dug in the ground which is relatively deep and smaller compared to pond and provides clean water for everyday uses particularly for drinking.
41 Kucha means made of bamboo or other temporary establishment which are not strong.
213 VI. Washing Mother’s Hand after Defecation (WH)
Maintaining a healthy and hygienic environment at home and practicing healthy food habits have significant impacts on child nutrition. These do not incur any large expense for the household but rather depend on members having health and nutrition related knowledge. In Bangladesh, due to lack of this knowledge mothers are not very serious about their own hygiene and disposal of children‟s excreta for those who cannot use the toilets of adults (BBS 2002b). This behaviour eventually can cause various diseases to the children, sometimes leading to death. Therefore, hygienic practices are essential for protecting children from malnutrition. The variable washing mother‟s hand falls into a „yes‟ or „no‟ category, which is expected to have a positive impact on child nutrition and thus the sign of this variable is expected to be positive.
VII. Distance to Health Centres from the Village (HC)
In the context of rural areas, location of health centres is important as these provide information on treatment of diseases, relevant medicine and health services, contact with doctors, attendants and health workers and thus, importantly things done in a shorter time so not resulting in delay in taking the patient to hospital (as discussed in Chapter 4). Whenever children get sick, those who reside nearby the health centres are usually provided with more treatment than those who reside far from the health centres. In order to capture the effect of village characteristics on child nutrition, the variable, distance to health centres is considered. Thus, the Equation 5.15 includes one more variable „distance to health centres (HC)‟ in its new specification. The government district hospital, thana health complex, union health and family welfare centre, satellite clinic, private hospital/clinic, doctor‟s chamber and dispensary are included in „health centres‟ when the variable „distance to health centres‟ is considered (as discussed in Chapter 4) for the nutrition model. If any centre located nearby the village within 1 to 1.5 kilometres, the variable takes the value „0‟ in calculating the travel time to reach the health centres. Otherwise, the distance is measured by travel time to reach the nearest health centres, which is given in the data set. Therefore, average travel time to reach the health centres is used in measuring the effects of „distance to health centres‟ on child nutrition. „Distance to health centres‟ from the village is expected to be negatively related with child nutrition and thus the sign would be
214 negative. In other words, nearer location of health centres to the villages improve child nutrition.
7.2 Estimated Results on Child Nutrition: National Level
The estimated results obtained from a national perspective, using multiple regression model Equation 5.15 in Chapter 5, are explained in Table 7.1 and Table 7.2. These equations are solved by using the „ordinary least square (OLS)‟ method. The F statistic, which measures the overall fitness of the model provides a test of the null hypothesis (H0) that the slope coefficients are simultaneously zero. If the computed F statistic exceeds the critical value, this suggests rejection of the null hypothesis at certain level of significance (e.g. = 0.01). In other words, this indicates that the Equation 5.15 is good fit in explaining child nutrition. Alternatively, if the p-value of the observed F statistics is sufficiently low for rejecting the null hypothesis (H0), this means at least one independent variable influences child nutrition. The t-values show the significance of the individual estimates and the corresponding p-values also indicate the significance of these estimates where three levels: 1 percent, 5 percent and 10 percent of significance are considered for drawing prediction of the variables. In the child nutrition data set, the total number of children (from 6 to 71 months) is 4,000. The variable „distance to health centre‟, indicating a village characteristic is included in the model for rural areas and thus, the matching number of rural children is 1,601. The estimated results from rural perspective are reported in Table 7.3 and 7.4 respectively.
7.2.1 Effects on Stunting: National Level
The estimated results on stunting as presented in Table 7.1 shows that the F statistics is 17.58, which is large enough to reject null hypothesis and the corresponding p-value (alternative measure) is also sufficiently low for rejecting the null hypothesis. This indicates that the specified model is good for explaining the probability of a child being stunted. The estimated R-squared is around 9 per cent which is comparatively low. However, this has secondary importance (Gujarati 2003; Lincove 2009) in the investigation of social fields. The number of children at national level is 4000 and the detailed results at the national perspective are presented in Table 7.1.
215 Table 7.1: Child Nutrition in terms of Stunting (HAZ): National Level
Independent Variables Coefficient t-values
Daily per capita Calorie Consumption (DCC) .0003*** 5.64
Father’s Education (FE) No education (reference level) - - Primary level .05 1.05 Lower secondary .003 0.32 Higher secondary .19** 2.15 Graduate & above .24 1.54
Mother’s Education (ME) No education (reference level) - - Primary level .09 1.32 Lower secondary .20** 2.28 Higher secondary .44*** 3.92 Graduate & above .97*** 4.36
Girl’s Status in Stunting (Sex2) -.01 -0.21
Sources of Drinking Water (Dr) Tube-well (shallow) .17 1.17 Tube-well (deep) -.05 -0.28 Tap .35** 2.09 Well .25 1.06 Pond/river (reference category) - -
Types of Toilet (TT) Flush/sanitary/water sealed .68*** 8.72 Pit latrine (excreta disposed under ground) .25*** 4.46 Pit latrine (excreta not disposed under ground) .17** 2.18 Fixed kucha latrine (reference category) - - Hanging Latrine .03 0.18 Open Space (no fixed place) -.07 -0.12
Washing Mother’s Hand (WH) -.36 -1.50
Constant -2.52 -8.83
F (20, 3979) = 17.58 Prob > F = 0.000
[H0: β2 = β3 = β4= …… β9 = 0 (no linear relationship); H1: at least one independent variable influences underweight]
R-squared = 0.086 Number of observation = 4000
Level of Significance * p < 0.10- significant at 10 % level; ** p < 0.05- significant at 5 % level; and *** p < 0. 01; significant at 1% level.
Note: In multiple regression model, no z-score is applicable, thus no z-score is given in this table while t-values are given for individual estimates.
Source: Authors calculation based on HIES, 2000 and CNS 2000, BBS, Ministry of Planning, Government of Bangladesh.
216 Table 7.1 shows that HAZ is significantly positively associated with daily per capita calorie consumption at 1 percent level of significance meaning HAZ improves (alternatively, stunting reduces) if daily per capita calorie consumption increases, holding all other variables constant. The estimate of father‟s having primary education or lower secondary education, which is most frequent, shows no significant effects on stunting although the less common higher secondary education is significant. On the other hand, mother‟s educations at all levels except primary education have significant effects in reducing the probability of a child being stunted. Sources of drinking water such as shallow tube-well or water from a well have no significant effects while tap water has the highest effect in reducing child‟s stunting as compared with pond/river water (non-safe sources). The effects of using hygienic toilets such as flush latrine, pit latrine (excreta disposed underground), pit latrine (excreta not disposed underground) on stunting are significant as compared with non-hygienic fixed-kucha latrine (excreta not well managed). In protecting children from stunting, washing mother‟s hand has no significant impact although the association is positive. These are discussed in detail in section 7.4.
7.2.2 Effects on Underweight: National Level
It is observed from the estimated results shown in Table 7.2 that the F statistics is 14.01, which is large enough and the corresponding p-value is also sufficiently low for rejecting H0 at 1 per cent level of significance. The estimated R-squared for underweight is around 9 per cent indicating the proportion of variation in underweight explained by the independent variables. For individual estimates, levels of significance (1%, 5% and 10%) are indicated by the number of asterisks. It is observed from Table 7.2 that at the national level, underweight (WAZ) responds almost in a similar way as stunting (HAZ) responds with daily per capita calorie consumption, parent‟s education, types of toilet used by the household members and washing mother‟s hand. The exception is observed only in the case of sources of water, in which tap water has some positive effect in protecting children from stunting but there is no effect in protecting children from underweight at all. The detail results are shown in the following Table 7.2.
217 Table 7.2: Child Nutrition in terms of Underweight (WAZ): National Level
Independent Variables Coefficient t-values
Daily per capita Calorie Consumption (DCC) .0002*** 5.28
Father’s Education (FE) No education (reference level) - - Primary level .05 0.98 Lower secondary -.01 -0.20 Higher secondary .11* 1.75 Graduate & above .07 0.52
Mother’s Education (ME) No education (reference level) - - Primary level .04 0.81 Lower secondary .15** 2.13 Higher secondary .32*** 3.91 Graduate & above .56** 2.46
Girls Status in Nutrition (Sex2) -.01 -0.37
Sources of Drinking Water (Dr) Tube-well (shallow) .16 1.37 Tube-well (deep) .11 0.92 Tap .20 1.54 Well .16 0.97 Pond/river (reference category) - -
Types of Toilet (TT) Flush/sanitary/water sealed .55*** 9.39 Pit latrine (disposed under ground) .22*** 5.39 Pit latrine (not disposed under ground) .16*** 3.13 Fixed kucha latrine (reference category) - - Hanging latrine -.16 -0.16 Open space (no fixed place) -.03 -0.03
Washing Mother’s Hand (WH) -.09 -0.49
Constant -2.55 -10.90
F statistic (20, 3979) = 14.01 Prob> F = 0.000 [H0: β2 = β3 = β4= …… β9 = 0 (no linear relationship); H1: at least one independent variable influences underweight] R-squared = .085 Number of observation = 4000
Level of Significance p < 0.10; significant at 10 % level; ** p < 0.05; significant at 5 % level; and *** p < 0. 01; significant at 1% level
Note: Estimation employs household level data from the CNS 2000. Standard errors are corrected for heteroscedasticity using „robust option‟.
Source: Author‟s calculation based on HIES, 2000 and CNS 2000, BBS. M/O Planning, Government of Bangladesh.
218 7.3 Estimated Results on Child Nutrition: Rural Area In order to examine the status of child nutrition from the rural perspective, the variable „distance to health centres‟ was included in Equation 5.15. The estimation procedure employs household level nutrition data from CNS 2000 was merged with village characteristics from the community survey data 2000 included in HIES 2000. As a result, the total number of matching children at the rural level is 1601. The estimated results of HAZ and WAZ are given in Table 7.3 and in Table 7.4 respectively.
7.3.1 Effects on Stunting: Rural Area The status of children‟s stunting from the rural perspective is shown in Table 7.3. The obtained F-statistics is 23, which is sufficiently high and the corresponding p-value is also sufficiently low for rejecting the null hypothesis. This indicates that the specified model is good for explaining child nutrition in terms of stunting. At the rural level, the obtained R-squared is 25 per cent which indicates the proportion of variation in stunting explained by the independent variables included in the model. Importantly, the value of R-squared (0.25) at the rural level is larger than the value (0.09) obtained for the national level. However, the detailed results of stunting in the rural areas are presented in Table 7.3.
219 Table 7.3: Child Nutrition in terms of Stunting (HAZ): Rural Area Independent Variables Coefficient t-value
Daily per capita Calorie Consumption (DCC) .0003*** 5.05
Father’s Education (FE) - - No education (reference level) -.31 -2.92 Primary -.01 -0.08 Lower Secondary -.06 -0.35 Higher Secondary .47* 1.84 Graduate & above
Mother’s Education (ME) No education (reference level) - - Primary .40** 2.94 Lower Secondary .78*** 4.80 Higher Secondary -.32 -1.71 Graduate & above .49* 1.72
Girls’ Status (Sex2) .02 0.21
Sources of Drinking Water (Dr) Tube-well (Shallow) 4.32*** 36.26 Tube-well (Deep) 4.31*** 20.62 Tap 4.39*** 34.71 Well 4.13*** 14.38 Pond/river (reference category) - -
Types of Toilet (TT) Flush /Sanitary/ Water seal 1.44*** 13.73 Pit Latrine (under the ground) .42*** 3.70 Pit Latrine (open space) .87*** 7.22 Fixed kucha (reference category) - - Hanging Latrine .81*** 3.82 Open Space .64*** 5.12
Washing Mother’s Hand (WH) -.49 -3.02
Distance to Health Centre (HC) -1.06** - 2.22
Constant -7.03 -20.56
F statistic ( 21, 1579) = 23.00 Prob > F = 0.000
[ H0: β2 = β3 = β4= …… β9 = 0 (no linear relationship); H1: (at least one independent variable affects stunting)]
R-squared = 0.25 Number of observation = 1601
Level of Significance p < 0.10- significant at 10 % level, ** p < 0.05- significant at 5 % level, and *** p < 0. 01- significant at 1% level
Note: Estimation employs household level data from the CNS 2000 Standard errors are corrected for heteroscedasticity using „robust option‟.
Source: Author‟s calculation based on HIES and CNS 2000, BBS, Ministry of Planning, Government of Bangladesh.
220
Table 7.3 shows that the probability of a child being stunted is significantly reduced by daily calorie consumption at 1 per cent level, keeping all other effects constant. In reducing the prevalence of stunting, all levels of mother‟s education except higher secondary have significant impacts compared to mother‟s having no education. While in the case of father, primary levels to higher secondary education yield no effects although graduate fathers have some influence in this respect. At the rural areas, all sources of drinking water have highly significant effects in reducing stunting. But unexpectedly, the uses of types of toilets particularly „hanging latrine‟ and „use of open space‟ have no systematic effects in reducing the probability of a child being stunted. In protecting children from stunting, washing mother‟s hand has no positive impact while the variable „distance to health centres‟ from the village significantly reduces the prevalence of stunting. 7.3.2 Effects on Underweight: Rural Area
The status of child nutrition exposed by underweight at the rural level is shown in Table 7.4. The obtained F-statistics and the corresponding p-value indicate that the specified model is good as a whole. The value of R-squared at the rural level is 0.29, which indicates the proportion of variation in underweight explained by the independent variables. Noticeably, the value at the rural level (0.29) is large than the value (0.09) of the national. It is observed from the Table 7.4 that at the rural level, underweight (WAZ) is unusually negatively associated with daily calorie consumption. Although the estimate is not significant, this behaviour is explained by the seasonal factors at the rural level. Similar to stunting (HAZ), father‟s education at any level does not have any effect on underweight while mother‟s all levels of education except primary have significant impacts in reducing underweight. All sources of drinking water significantly reduce the prevalence of underweight compared to non-safe pond/river water. Again, the use of toilets particularly „open space‟ does not have any systematic effect in reducing underweight compared to fixed kucha latrine. Unusually, washing mother‟s hand has no positive effect in reducing children‟s underweight. Although the coefficient is significant, however, it has no importance due to having unexpected (negative) sign. Distance to health centres from the village deteriorates the prevalence of underweight or in other
221 words, nearer location of health centres from the village improves the prevalence of underweight. These results are explained in detail in section 7.4. Table 7.4: Child Nutrition in terms of Underweight (WAZ): Rural Area Independent Variables Coefficient t-value
Daily per capita Calorie Consumption (DCC) -.0001 -1.58
Father’s Education (FE) No education (reference level) - - Primary -.16 -2.22 Lower Secondary -.19 -1.51 Higher Secondary .12 1.15 Graduate & above .11 0.60
Mother’s Education (ME) No education (reference level) - - Primary .10 1.05 Lower Secondary .76*** 6.10 Higher Secondary .17 1.42 Graduate & above 1.03*** 4.42
Girls (Sex2) -.08 -1.59
Sources of Drinking Water (Dr) Tube-well (Shallow) 1.88*** 19.07 Tube-well (Deep) 1.95*** 13.19 Tap 1.95*** 16.10 Well 1.83*** 7.3 Pond/river (reference category) - -
Types of Toilet (TT) Flush /sanitary/ water seal .97*** 12.20 Pit Latrine (under ground) .36*** 4.21 Pit Latrine (open space) .50*** 6.24 Fixed kucha (reference category) - - Hanging Latrine .24 1.04 Open Space .33*** 3.43
Washing Mother’s Hand (WH) -2.71 -22.95
Distance to Health Centre (HC) -.77** -2.65
Constant -1.69 -6.70
F statistic ( 21, 1578) = 19.00 Prob > F = 0. 000
[H0: β2 = β3 = β4= …… β9 = 0 (no linear relationship); H1: (at least one independent variable affects stunting)]
R-squared = 0.29 Number of observation = 1601
Level of Significance : * p < 0.10- significant at 10 % level, ** p < 0.05- significant at 5 % level, and *** p < 0. 01 significant at 1% level
Note: Estimation employs household level data from the CNS 2000, merged with relevant community characteristics –village level data. Standard errors are corrected for heteroscedasticity using „robust option‟.
Source: Author‟s calculation based on CNS 2000, BBS, Ministry of Planning, Government of Bangladesh.
222 7.4 Explanation of Estimated Results of Independent variables The regression results of child nutrition in terms of stunting (HAZ) and underweight (WAZ) at the national level are shown in Table 7.1 and in Table 7.2 while the results at the rural areas are shown in Table 7.3 and in Table 7.4. It is important to note that both stunting and underweight respond mostly to a similar direction and apparently, the magnitudes of coefficients of stunting are larger than those of underweight. Detail explanation of the estimated results is as follows.
7.4.1 Effect of Daily per Capita Calorie Consumption It is observed that daily calorie consumption (per person/ per child) significantly reduces the probability of a child being either stunted or underweight at the national level. However, exception is observed in the case of underweight at the rural areas, the probability of a child being underweight is negatively associated with daily calorie consumption (Table 7.4). This behaviour is explained by the consumption pattern of the rural people. In rural Bangladesh, calorie consumption fluctuates significantly during the off peak and peak period of harvesting (as described in Chapter 4). During the off peak season, daily average calorie consumption is substantially lower than that of peak season (HKI Bangladesh 1998). The effect of this fluctuation in calorie consumption may reflect in the prevalence of children‟s underweight in the rural areas. However, apparently, the coefficient of daily calorie consumption tends to be larger in the case of stunting than that of underweight which indicates the effect of stunting is long enduring than the sort term effect of underweight.
7.4.2 Effects of Parent’s Education At the national level, it is observed from the estimated results (as shown in Table 7.1 and Table 7.2) that all levels of mother‟s education except primary education significantly reduce the probability of a child being either stunted or underweight. The coefficients are consistently higher as the level of mother‟s education increases. Among the levels of education, mothers with graduation and above education have the highest impact on nutrition by protecting children from stunting (0.97) and underweight (0.56) compared to mothers having no education. But in the case of fathers, only higher secondary education has some positive effects in reducing the prevalence of malnutrition.
223 Similarly, at the rural level, all levels of mother‟s education except higher secondary have significant effects in improving child nutrition while father‟s education has no impact in this regard. From the estimated coefficients, apparently, the impacts of mother‟s education are larger than those of father‟s education in reducing the prevalence of child malnutrition (stunting or underweight). This finding is supported by Barrera (1991) who found positive association with mother‟s education by using the determinant child‟s
height-for-age. As one of the major hypotheses of this thesis is that mother‟s education significantly influences child nutrition, to explore the empirical evidence, the regression was performed considering only parent‟s (father and mother) individual levels of education while all other independent variables in Equation 5.15 were restricted. Thus, the estimation procedure involves two regressions based on the unrestricted models (Table 7.1 for HAZ and Table 7.2 for WAZ) and the restricted models (Table 7.5 for HAZ and Table 7.6 for WAZ). The F-ratios obtained from the restricted equations and the unrestricted equations based on the null hypothesis (H0) that the results of restricted models are accepted at a certain level (e.g.1%, 5%, 10%) of significance. For individual coefficients, predictions are made depending on t-values and the corresponding p-values considering the three (1%, 5%, 10%) levels of significance. In addition, the comparative strength (for example beta coefficient) of each coefficient of parent‟s individual levels of education on stunting and underweight were also estimated to substantiate the individual effect of either father‟s or mother‟s education on child nutrition. Detailed explanation is given below.
I. Father’s and Mother’s Education: Comparative Effects on Stunting The coefficients of parent‟s individual levels of education on stunting shown in Table 7.5 which are estimated by putting restriction on Equation 5.15 considering father‟s and mother‟s individual levels of education only. The value of R-squared for restricted model is .054 and the value of R-squared for unrestricted model is .09 (as shown in Table 7.1). Based on these two, the computed F-ratio is 20, which exceeds the critical value of F and thus the null hypothesis is rejected at 1 per cent level of significance. Parent‟s individual levels of education however, explain the probability of a child being stunted. Further, „beta coefficients‟ are calculated based on the restricted model in order to assess
224 the relative strength of parent‟s individual levels of education. For stunting, the estimated coefficients (raw) and the beta coefficients are given in Table 7.5.
Table7.5: Parent’s Education and Stunting (HAZ) Education Level Coefficient (raw) Beta Coefficient Father Mother Father ♣ Mother♣ Primary Level .10 .14* .02 .03* (1.44) (1.80) (1.44) (1.80) Lower Secondary .08 .30*** .02 .06*** (0.86) (3.35) (0.86) (3.35) Higher Secondary .32*** .64*** .08*** .13*** (3.72) (6.12) (3.72) (6.12) Graduate and above .51*** 1.25*** .06*** .09*** (3.09) (4.71) (3.09) (4.71)
[F statistics ( 8, 3991) = 28.58; Prob > F = 0.0000
H0 : β2 = β5 = β6 = β7 = β8 = β9 = 0; H1: β3 and β4 not equal to zero
R-squared for restricted model = 0.054 R-squared for unrestricted model = 0.09
The computed F-ratio = (.09-.054 )/8 / (1-.09)/3980 = 20 Number of observation: 4000 Level of significance * p < .10 - significant at 10 %, ** p < 0.05 - significant at 5 % , ***p < 0.01- significant at 1 %
Note: „No education‟ is treated as reference level. Figures in parenthesis are t-values. Figures in bold indicate that effects are statistically significant due to one unit change in father‟s as well as mother‟s education. ♣ - indicates beta coefficient has the same t-value as raw coefficient. Source: Author‟s calculation based on HIES, 2000, and CNS, 2000 BBS, Government of Bangladesh.
In Table 7.5, coefficients (raw) indicate that father‟s and mother‟s individual levels of education which are estimated based on Equation 5.15 by dropping all other variables except parent‟s education. Apparently, these coefficients (restricted model) are likely to larger compared to the coefficients of unrestricted model (Table 7.1). More specifically, children are apparently expected 0.32 less likely to be stunted if fathers have higher secondary education holding all other factors constant. The effect is much higher (0.51) in the case of father with graduate and above levels of education. By contrast, in the case of mother, all levels of education have significant effects in reducing the probability of a child being stunted compared to mother‟s having no education. Apparently, the coefficients are consistently larger from low level to higher education and the largest
225 coefficient is observed at graduate and above levels of education. Children are expected 0.64 less likely to be stunted if mothers with higher secondary education compared to mother's having no education and the response is considerably higher (1.25) in the case of mother with graduate and above levels of education. It is important to note that in Table 7.5, column indicated 'beta coefficients' shows the same standardised unit of raw coefficient by which regression coefficient could be compared to assess the relative strength of each predictor (UCLA Academic Technology Service 2011). The detail results are included in Appendix Table A19.7. In Table 7.5, mother‟s higher secondary education has the largest beta coefficient 0.13 (in absolute value) and the father with primary or lower secondary education has the smallest beta co- efficient 0.02. A one standard deviation increase from lower secondary to higher secondary in mother‟s education leads to a 0.13 standard deviation increase in predicted HAZ and thus having a positive effect in reducing child‟s stunting, with other variables held constant. Similarly, a one standard deviation increase in father„s education from lower to higher secondary education leads to a .08 standard deviation increase in predicted HAZ, thus resulting in an improvement in reducing child‟s stunting. Nonetheless, caution is needed in interpreting the difference between the raw coefficient and beta coefficient. For example, to describe father‟s primary education for raw coefficient, it is necessary to say “A one-unit in primary education would yield a .10-unit increase in the predicted HAZ or reducing child‟s stunting” while for the standardized coefficient (beta), it would be “A one standard deviation increase in father‟s primary education would yield a 0.02 standard deviation increase in the predicted HAZ or reducing child‟s stunting”.
II. Father and Mother’s Education: Comparative Effect on Underweight Similarly in the case of underweight, the value of R-squared for the restricted model is .05 (as shown in Table 7.6) while it is .09 for unrestricted model (as shown in Table 7.2). The resultant F-ratio is 25, which is large enough to reject null hypothesis and thus indicates that the restricted model is acceptable in explaining the probability of a child being underweight at 1 per cent level of significance. The estimated coefficients (raw) and beta coefficients of father‟s and mother‟s education based on restricted model
226 (Equation 5.15) considering only parent‟s individual levels of education are presented in Table 7.6.
Table 7.6: Parent’s Education and Underweight (WAZ)
Education Level Coefficient (raw) Beta Coefficient
Father Mother Father♣ Mother♣ Primary Level .09* .08 .03* .03 (1.83) (1.48) (1.83) (1.48) Lower Secondary .04 .24*** .01 .07*** (0.61) (3.72) (0.61) (3.72) Higher Secondary .20*** .48*** .07*** .13*** (3.29) (6.44) (3.29) (6.44) Graduate and above .27** .78*** .05** .07*** (2.32) (4.06) (2.32) (4.06) F( 8, 3991) = 25.59; prob > F = 0.0000
H0 : β2 = β5 = β6 = β7 = β8 = β9 = 0; H1: β3 and β4 not equal to zero R-squared for restricted model = .05 R-squared for unrestricted model = .09 The computed F-ratio = (.09-.05 )/8 / (1-.09)/3980 = 25 Number of observation: 4000 Levels of Significance: * p < 0.10 - significant at 10 %, ** p < 0.05 - significant at 5 % , *** p < 0. 01 - significant at 1%
Note: No education is considered as reference category. Figures in parenthesis are t-values. Bold figures indicate that effects are statistically significant due to one unit change in father‟s as well as mother‟s education. ♣ - indicates Beta coefficient has the same t-value as raw coefficient.
Source: Author‟s calculation based on HIES 2000 & CNS 2000 BBS, Government of Bangladesh.
Similar to stunting shown in Table 7.5, Table 7.6 also presents both raw coefficients and the beta coefficients to explain the probability of a child‟s underweight associated with parent‟s individual levels of education. However, according to raw coefficients, children are expected 0.20 less likely to be underweight if a father‟s having higher secondary education compared to a father having no education. The effects are much higher (0.27) if fathers have graduate and above levels of education but the coefficient is significant at 5% level. In the case of mother‟s education, all levels of education except primary have significant effects in reducing child‟s underweight. Children are apparently 0.24 less
227 likely to be underweight if mothers have lower secondary education compared to mothers having no education. The effects are significant and consistently larger in reducing underweight if mothers have higher secondary (0.48) and graduate and above levels
(0.78) of education and the coefficients are significant at 1% level. Similarly to stunting, beta coefficients in Table 7.6 show the relative strength of each predictor (shown in appendix Table A20.7) to compare the effects of father‟s and mother‟s individual levels of education on underweight. Mother‟s higher secondary education has the largest beta coefficient, 0.13 (in absolute value) while father‟s lower secondary education has the smallest beta co-efficient of 0.01. Thus, a one standard deviation increase in mother‟s education from lower secondary to higher secondary leads to a 0.13 standard deviation increase in predicted WAZ, leading to an improvement in child‟s underweight. In the case of father‟s education, lower secondary education leads to a 0.01 standard deviation improvement in predicted child‟s underweight (WAZ) compared to fathers having no education. According to raw coefficient, if mother‟s has lower secondary education, children‟s are 0.24 less likely to be underweight, while for the standardized coefficient (beta), it yields a 0.07 standard deviation improvement in the predicted child‟s underweight. However, apparently, the coefficients of HAZ are larger than those of WAZ. It is shown from this analysis that both father‟s and mother‟s individual levels of education particularly secondary and above education contribute significantly in protecting children from malnutrition while the effects of mother‟s education are stronger than those of father‟s education as evident by the „beta coefficients‟. Thus, the hypothesis
H3 is supported by this empirical investigation. This proposition is also supported by the World Bank‟s MDG Report (2005), which mentioned that maternal schooling had a strong association with underweight rates, with each additional year of schooling of the mother associated with a decline of about 2 percentage points in the child underweight rate. The report of CNS 2000 stated that mother‟s literacy is negatively related with the prevalence of malnutrition (BBS 2002b). According to the BDHS 2004, under-five mortality declined sharply with increased level of mother‟s education, the rate is almost 40 per cent lower for children whose mother‟s have at least some secondary education compared with those who have no education (NIPORT 2005). After controlling all other
228 effects, children with mothers who had never attended school, 55 per cent was expected to suffering from stunting and 56.8 per cent was in the case of underweight. However, among the children with mothers having School Secondary Certificate or above, the prevalence of stunting was expected to 21 per cent and it was 24 per cent in underweight, holding all other factors constant (World Bank 2005). Zayed, Stopnitsky & Khan (2006) also showed that the secondary education of a mother significantly reduces the probability of a child being malnourished irrespective of the wealth status of the family. They, therefore, suggested that female education up to secondary level is the key policy indicator to address the child malnutrition in Bangladesh. In assessing the need of child nutrition in Bangladesh, the Report of Millennium Development Goals Need Assessment and Costing 2009-2015, Bangladesh suggested particularly that mother‟s education is important in order to improve the current dreadful situation in child nutrition prevailing in Bangladesh (GoB 2009b). Thus, a considerable number of studies support the contribution of mother‟s education in improving child nutrition. 7.4.3 Effect of Girls’ Status (Sex2) in Child Nutrition To analyse the differential effects between boys and girls (aged 6 to 71 months), the hypothesis H4 is developed based on the assumption that girls are discriminated in daily calorie consumption compared to boys. The estimated results do not show any systematic effects on girls‟ deprivation. Both in the case of stunting and underweight, girls are likely to be more deprived compared to boys‟ although the coefficients are not statistically significant. In rural areas, it seems girls are less likely to be stunted but the coefficient, again is not statistically significant. Therefore, although, there is a tendency of girl to consume less than boy, but no significant difference is observed between the two in daily calorie consumption. Thus the assumption of higher probability of girls being more malnourished by either being stunted or underweight compared to boys (H4) is not supported by this particular data set. This was also reported by the World Bank‟s MDG Report (2005), which found no significant gender difference in nutritional status
between boys and girls in Bangladesh.
7. 4.4 Effect of Sources of Drinking water The results on individual sources of water show that only tap water has a positive association with stunting compared to the household using non-safe pond water or river
229 water and the result is significant at 5 per cent level. After controlling all other effects, water sources of deep tube-well and water from well (small and deep) have no effects on the probability of a child being stunted. Unusually, at the national level, sources of drinking water have no significant effects in protecting children from being underweight compared to the household using drinking water from pond or river. This is may be due to the majority of urban residents do not use pond or river water rather they use tap water mainly. While at the village level, it is observed that all categories of water sources have significant effects in protecting children from being stunted and underweight. The associations are apparently, significantly stronger (as shown in Tables 7.3 and 7.4) than those of the effects observed at the national level. This strong association between water sources and child nutrition may be explained in Bangladesh that more than 90 per cent rural people use safe drinking water mainly from shallow tube-well and deep tube-well. Thus, the results are significant when these are compared with the households using drinking water either from pond or river. The effect of arsenic contamination is not taken into account in this regard although this a serious concern particularly in the case of tube-well water in rural Bangladesh (BBS & UNICEF 2009). This issue was not included in HIES 2000 and CNS 2000 data set. However, the general results are supported by Hill and King (1993) who mentioned that it was evident in the Philippines that mother‟s schooling and the availability of safe drinking water improved health condition of children from chronic malnutrition while household income did not. 7.4.5 Effect of Types of Toilet used by the Household The analysis shows that flush/sanitary/water sealed toilets offer the best protection against child malnutrition, followed by pit latrines. After controlling all other effects, use of flush/sanitary latrine likely to result in a decline in children‟s stunting (as shown in Table 7.1) and underweight (as shown in Table 7.2) compared to those who use non- hygienic fixed kucha latrine (excreta not well managed). Therefore, the regression results support the use of hygienic toilets as having significant effects in improving nutrition compared to non-hygienic toilets particularly at the national level. Unexpectedly, at the rural level, the use of non-hygienic types of toilet by the household members has no
230 systematic effect on child nutrition. However, the estimates of national level are supported by the findings of other studies. The children who use open spaces for their sanitation needs are twice as likely to be underweight as those who use flush toilets (World Bank 2005). The report of BBS and UNICEF (2009) mentioned that rural people in Bangladesh are now much aware in using toilet and thus help in improving child nutrition.
7.4.6 Effect of Washing Mother’s Hand At the national level, the analysis shows unusually that the variable washing mother‟s hand after defecation has negative associations with stunting as well as underweight (as shown in Tables 7.1 and Table 7.2). This means that child nutrition is not influenced by this variable. The results at village level, both in the cases of stunting and underweight, the associations are not only negative and but the coefficients are also significantly high (as shown in Tables 7.3 and Table 7.4).
Although, most mothers (99%) in the sample population reported that they washed their hands after defecation and 66 percent of them used soap while washing hands, the analysis shows the negative association of washing mother‟s hand with child nutrition, which is not expected. Although a considerable proportion of mothers reported using soap, but this may not be truly reported. However, although, if most mothers wash their hands, then the negative effects of not washing hands will not show up. Thus, washing mother‟s hand protects children from malnutrition is not sufficiently supported by this particular data set.
7.4.7 Effect of Distance to Health Centres The analysis suggests that the distance measured as an average travel time to reach the nearest health centres such as district hospitals, thana42 health complex, union health centres, dispensary and doctor‟s chamber/clinic (discussed in Chapter 4) from the village significantly deteriorates both stunting and underweight. Holding all other variables constant, children are likely to be highly stunted (1.06) due to a one unit change in average travel time to reach the health centres. Similarly, in the case of underweight, the response is recorded as 0.77 holding all other variables constant. This indicates that if
42 Union is the lowest and Thana is working as second lowest administrative unit in Bangladesh.
231 distance to health centres is longer, the prevalence of malnutrition is higher while conversely proximity of health centres to the village declines the prevalence of malnutrition. Therefore, in the villages located far from the health centres, educated mother could be a substitute for receiving health services for the children. Thus, this result could be compared by the study of Barrera (1990), who found that the mother‟s education had a larger protective effect on child health in communities that were farther from health care facilities than in areas that were better off.
7.5 Explanation of Hypothesis The analysis provides important insights into child nutrition in that both stunting and underweight behave generally in a similar direction in response to independent variables. Importantly, in most cases, the coefficients of a child being stunted (long term) are larger than the coefficients of a child being underweight (short term) which indicates that the long term effect on nutrition is stronger and enduring than the short term effect. Thus, deprivation in nutrition affects children‟s height and thus reduces future productivity. However, in respect to major findings, the explanation of hypotheses H3 and H4 is important and given below:
H3: mother’s education significantly influences a child’s nutritional status in terms of stunting or underweight
The estimated results obtained from the regression analysis indicate that mother‟s education at all levels from lower secondary to graduation and above except primary education significantly improve child nutrition both in terms of stunting (HAZ) and underweight (WAZ). The coefficients are also consistently higher as the level of mother‟s education increases compared to mothers having no education. The highest responses are observed if mothers have graduate and above level of education and the coefficients are 0.97 for stunting and 0.56 for underweight particularly at the national level (as shown in Tables 7.1 and 7.2). At the village level, mother‟s education has also significant effect in protecting children from malnutrition. By contrast, father‟s education has very little impact on child nutrition both at the national and the rural level.
232 The estimated `beta coefficients‟ (as shown in Tables 7.5 and 7.6) which indicate the comparative strength of the individual coefficients show that both father‟s and mother‟s higher secondary education contribute significantly in protecting children from malnutrition. But the effects of mother‟s education are stronger than those of father‟s education as evident by the larger „beta coefficients‟. Therefore, it can be said that the hypothesis H3 is supported by this analysis. The other hypothesis regarding child nutrition is:
H4: a girl child’s lower calorie intake per day leads to a higher probability of her being malnourished (either stunted or underweight)
The estimated coefficients Sex2 although show that HAZ and WAZ have negative association with girl‟s nutrition meaning a girl child is more malnourished in terms of stunting and underweight compared to a boy but the results are not significant. In the case of HAZ, the exploration of relation between Sex2 and the variable daily calorie consumption (DCC) shows that although a girl child consumes less calories compared to a boy child but no significant deprivation occurred between boy and girl in consuming daily calories at household level (shown in Appendix Table A21.7). Thus, the hypothesis
H4 however, may not be supported by this data set.
7.6 Concluding Remarks The analysis reported in this chapter reveals that daily calorie consumption plays a significant role in reducing child malnutrition, although there remains exception in the prevalence of underweight at the village level, which may occur due to seasonal factors. In order to counteract the peak and off peak situation regarding calorie consumption, mother‟s education may help to maintain a balance among the food items particularly for the children. However, nutrition and calorie intake are significantly related as would be expected, and this is closely related to household income. The analysis shows that mothers with at least lower secondary education have significant impacts in reducing the prevalence of a child being stunted or underweight. At the rural level also, mother‟s education has significant effect on child nutrition. By contrast, father‟s education has very little impact in protecting children from malnutrition either at the national or at the rural level. That for nutrition, unlike school attendance,
233 mother's education has a highly significant differential impact compared to fathers. The economic implication of this result is that child nutrition improved by the educated mothers enhances the productivity of children who will be the future workforce and this will in turn tremendously benefits the economy. Thus, the policy focus needs to be directed to take advantage of these relationships regarding child nutrition by investing more on women education. Although the girl child is more malnourished compared to boy child but the difference between boy and girl is not significant as indicated by the coefficient of Sex2. The individual source of drinking water has little impact on child nutrition at the national level while all sources of drinking water significantly improve the rural children‟s nutritional status. Similarly, individual type of toilets reduces the prevalence child‟s stunting or underweight significantly both at national level and rural level. Washing mother‟s hand has no significant effect in improving child nutrition rather unusually shows negative association which may be occurred due to false reporting by the mothers. At the village level, distance to health centre deteriorates the status of rural children‟s nutrition. This may encourage the role of educated mother to act as a substitute of health worker for the children at the villages located far from the health centres. However, as mothers spend more time with children, if they had adequate perceived health and nutritional knowledge, this could help them to improve child health by restraining communicable diseases and maintaining hygienic practices at household level. This process in turn results in a huge accumulation of human capital by improving nutrition and increasing learning capability and thereby improves the income potential of the children. Positive impacts of mother‟s education associated with child nutrition from this empirical analysis demonstrate that investing more on girls‟ education needed to be the priority in the country like Bangladesh to improve its long term development potential In the next chapter, conclusion of this thesis will be drawn introducing objectives of the study including a brief description of research process. The chapter will also discuss the major findings obtained from empirical analysis and policy guidelines as well as suggestions for future research particularly for Bangladesh as well other countries those having similar nature of development problem.
234
Chapter 8
Conclusion
First section of this chapter describes the objectives of the study including a brief analysis of research process. Section two presents the research questions and the respective hypotheses. Third section discusses the major findings regarding school attendance and nutrition while section four describes their policy implications. Fifth section outlines the specific contributions made by this study and highlights the issues further to be studied.
8.1 Objectives and the Research Process As Bangladesh is abundant with human resources with very limited natural resources, the country has undergone through implementing a number of policies and programs in the field of education, health, nutrition and social safety net programs in order to develop human resources. As a result, Bangladesh has achieved a considerable progress in reducing poverty level, infant and child mortality rate and enhancing primary school enrolment particularly girls‟ enrolment. Despite these achievements, more than one third of Bangladeshi citizen still lives below the poverty line and around half of the people remain illiterate. The most alarming is that around half of Bangladeshi children suffer either from moderate or severe malnutrition. Worryingly, the poor nutritional status of female children results in low birth weight babies who tend to be more malnourished in childhood and beyond. The high prevalence of child malnutrition and low levels of education acquired by the children together can cause an enormous wastage of potential productivity of the Bangladesh‟s future workforce. In this context, there is a pressing need for rigorous investigation in human capital theory and its application which was articulated in this thesis. This thesis investigated the
235 indirect contributions made by the educated women influencing decisions of children‟s school attendance and nutrition, which are usually been ignored by the traditional approach of labour market returns. However, this thesis was designed to achieve the following objectives by empirical investigation as introduced in Chapter 1:
Contribute to an understanding of the role women play in the economic development process of low income countries through their responsibility at the household level;
Investigate the impact of mother‟s education in achieving higher educational attainment as well as improved nutritional outcomes for children;
Provide a detailed analysis of the inter-relationships between female education and children‟s educational attainment and nutritional improvement;
Use the results of the study to develop policy recommendations applicable to Bangladesh and other similar developing countries to support investment in women‟s education, as a complement to other human capital policies and programs already in place.
In order to achieve these objectives, the methodology followed in this study consisted of two parts – the reviews of literature and the empirical investigation. Chapter 2 reviewed literature in the context of developing countries which embraced an evaluation of human capital theory, its determinants and application in women education, estimation techniques and the empirical findings of relevant studies in the context of developing countries. In addition, the situation of Bangladesh‟s education system, health and nutrition status, relevant policies, constraints and challenges were appraised. A comparison of these situations with other South Asian countries was also provided. These were described in Chapters 3 and 4. Based on an econometric framework for empirical investigation of research questions developed in Chapter 5 portrayed the process of estimation and the results on school attendance and child nutrition obtained by using the household survey data from Bangladesh were explained in Chapter 6 and Chapter 7 respectively.
236 8.2 Research Questions and the Hypotheses The human capital theory and its application to women‟s education showed that educated women generally contributed to the economy by their increased productivity through enhanced participation in the labour market. While in the context of developing countries, they are often found to be irregular in the labour market participation. It is caused either by their family responsibilities, or being attached to a wealthier family, or possibly constrained by the cultural aspects. This irregularity creates difficulty in assessing economic (direct) contributions of women‟s education. Due to absence of a unique measurement framework, the economic benefits of women‟s education still remain debatable. It was argued that even if educated women do not participate in labour market they were more likely to enhance their children‟s educational attainment and also improve their nutrition. This in turn accumulates extensive human capital of future work force and thus contributes significantly to the longer term economic growth. This motivation induced to develop research questions for empirical investigation in the context of Bangladesh because average literacy rate in Bangladesh is around 50 per cent and importantly, it is lower for women (45 per cent). At tertiary level, girls‟ participation is only one fourth of total student‟s enrolment. Education related data also showed that nearly 60 per cent of mothers had no education at all and among the literate mothers, most of them concentrated on primary education only. These mothers cannot utilize their knowledge to rear healthy and better educated children. Thus, the following relevant questions in relation to mother‟s education are developed as follows:
i) Whether women‟s education significantly enhances children‟s educational attainment?
ii) Whether women‟s education significantly improves children‟s nutritional status?
These questions were addressed by using econometric models. A probit model (Equation 5.9) was specified to estimate the effects of mother‟s education on children‟s school attendance while in the case of child nutrition, multiple regression model (Equation 5.15) was constructed. In both models, the focus was to find the influence of mother‟s education and thus, the level of parent‟s education was categorised into five levels in line
237 with Bangladesh‟s education system. These levels are: i) no education, ii) primary education, iii) lower secondary education, iv) higher secondary education, and v) graduate and above education. Based on these categories, the effects of parent‟s education on children educational attainment were assessed. The hypotheses developed in relation to school attendance and child nutrition are also described as follows:
H1: mother’s education significantly influences children’s school attendance
H2: there is a positive relationship between father’s level of education and boy’s school attendance and between mother’s level of education and girl’s school attendance
H3: mother’s education significantly influences a child’s nutritional status in terms of stunting or underweight
H4: a girl child’s lower calorie intake per day leads to a higher probability of her being malnourished (either stunted or underweight).
These hypotheses were tested through using standard „z’ scores and ‘t’ scores obtained from the estimated models. The significance of individual estimates was determined using three levels of (99%, 95% and 90%) of significance. By using data from Bangladesh Household Income and Expenditure Survey (HIES) 2000 and Child Nutrition Survey (CNS) 2000, the assigned model were solved. The effects of village characteristics on school attendance as well as on child nutrition were captured by using community survey 2000 data, which was included in HIES 2000 data set. The statistical package Stata Version-10 was used in manipulating and integrating data and estimating
the models.
8.3 Major Findings Obtained from the Analysis 8.3.1 Major Findings: School Attendance Based on Equation 5.9, children‟s school attendance was assessed mainly by the determinants of household income, parent‟s education with particular focus on mother‟s education, gender differentials and average expenditure of schooling including village
238 characteristics distance to school and supply of electricity to the rural households. Major
findings obtained regarding school attendance are given below: The analysis showed that children‟s school attendance was significantly positively associated with household income all other variables constant. Children are more likely to attend school if household income increases. If households are clustered into income quintile, the response on school attendance is not only significant but also increases sharply as the level of income increases. For example children from the richest group (5th quintile) respond at a greater rate in attending school compared to the children from poorest group (bottom quintile). This supports the findings obtained by Behrman and Knowels (1999) which showed on average, children from higher income groups started school a quarter of year (0.25) earlier, completed two more grades and scored significantly higher in the last completed grade than children from the lower income groups. Therefore, household income acts as an important determinant in achieving children‟s educational attainment. In the case of parent‟s education, both father‟s and mother‟s individual levels of education had significant influences on children‟s school attendance. Apparently, parents with secondary education yielded stronger effects than those of parents having primary education. These effects remained valid for both primary and secondary school attendance by the children. Unexpectedly, parent‟s having higher education (graduate and above level) had no significant impacts on children‟s school attendance. This counter intuitive result was explained by the fact that there were few parents those had graduate and above education in the data set and thus the number was not representative to yield any effect on school attendance. In exploring the question, 'does father‟s education impact on boy‟s school attendance or mother‟s education on girl‟s attendance' - the interaction variables showed no effects of father education on boy‟s school attendance or mother‟s education on girl‟s school attendance at primary level. While in the case of secondary school, father‟s higher secondary and above education had some impacts on boy‟s school attendance but mother‟s education at any level had no impact on girl‟s secondary school attendance particularly at the national level (as shown in Table 6.6).
239 Although it is difficult to distinguish exclusively the separate impact of mother‟s education on children‟s school attendance due to practice of associative partnering among educated men and women, the analysis manifested the most insightful observation that both father‟s and mother‟s education are equally important for a child‟s educational attainment. This supports the assertion made by Ermicsh and Francesconi (2001) that parent‟s education had a significant positive effect on the probability of school enrolment among both boys and girls. But the statement that mother‟s education was much more important than father‟s education in determining girls‟ enrolment decisions (Dostie & Jayaraman 2006; Glewwe & Jacoby 1994) - is not supported by this analysis.
Girl‟s status in educational attainment indicated by the variable Sex2 showed that girls were more likely to attend school compared to boys. However, for primary school the coefficient was significant at 10 per cent level while it was significant at 1 per cent in the case of secondary school. The coefficient of secondary school was apparently larger than that of primary school. This behaviour of girls‟ school attendance may be due to implementation of stipend programs and other incentives to encourage them to attend school longer particularly at secondary level to catch up the previous lag. Again, it was found that female headed household had no impact on children‟s primary school attendance at all while at the secondary level, although the effect was positive, the coefficient was not statistically significant. However, the effect of single parenthood particularly female headed household was not significant in this investigation as it was not very common in Bangladesh.
Year-wise age effect on „school attendance‟ showed some interesting insights. It was found that children‟s primary school attendance was unusually negatively associated with ages from 7 to 10 years both at the national and the rural level, but no particular reason for this behaviour was found. If year 11 and year 12 were included in primary school age group, these two years were positively associated with primary school attendance. By contrast, year 11 and year 12 were negatively associated with secondary school attendance indicating students took 6 to 7 years to complete the 5-year primary school cycle. As a result, they started secondary school at older (11 years or more) age and took more years to complete secondary school than the official age (11-15 years).
240 This is also supported by the findings of World Bank‟s MDG Report (2005) and the Education Watch Report published by CAMPE (2003). Although expenditure on schooling is an important determinant in educational attainment particularly for the poor families in Bangladesh, the empirical analysis did not reflect a specific reality faced by the poor households. It was seen from the correlation Table B5.1 in Annexure B5 that children‟s primary school attendance was unusually highly negatively correlated with average expenditure of schooling. However, interestingly, if this variable (average expenditure) was included in primary school attendance function, the coefficients of other variables were undermined and tended to be insignificant (shown in Appendix Table: A12.6). Therefore, the variable average expenditure on schooling was not included in primary school attendance function due to inconvenience. But at the secondary level, school attendance was significantly positively associated with average expenditure of schooling. This exposition was explained by the quality education which incurred high expenditure and thus had a positive association with school attendance. In the case of rural characteristics, the estimate of distance to school (DS) was neither negative for primary schools nor for secondary schools which were located in more distant places. This indicates that distance to school from the village had no impact on children‟s school attendance by this particular data set, which basically deters the girls‟ education as explained by Gannicott and Avalos (1994) and Glick (2008). It was mentioned by Bellew, Raney & Subbarao (1992) that separate schools for girls, boundary walls and toilet facilities are considered as crucial factors for improving girls‟ enrolment particularly at the secondary education in Bangladesh. Another analysis showed that electricity connection to households motivated rural children to attend school more (World Bank 2005) and thus had a positive impact on rural children's educational attainment. The above mentioned findings indicate that children of educated mothers were more likely to attend school than the children whose mothers had no education but the differential effect of mother‟s education compared with father‟s education was not strong. However, both father‟s and mother‟s education are equally important for children‟s educational attainment rather than mother‟s education does play a unique role in this
241 respect. This proposition supports the findings of Ermisch and Francessconi (2001), by whom it was shown that students performed good score when they had both fathers and mothers while students of single mother did not score well in higher study. Again, the estimated coefficients of interaction variables showed no evidence that mothers were more eager for their daughters‟ education while graduate fathers had some positive influences on their sons‟ school attendance particularly at secondary level. It is important to mention that government policies and programs provide financial support to girls‟ education play an important role in student's school attendance in Bangladesh and this is reflected in the coefficient Sex2, which showed girls were more
likely to attend school compared to boys and in some cases girls exceeded boys.
8.3.2 Major Findings: Child Nutrition Child nutrition was assessed through mainly the determinants of daily calorie consumption, parent‟s individual levels of education, girl‟s status in nutrition, sources of drinking water, types of toilet, washing mother‟s hand and distance of health centres from the village. In account of these influences, this thesis focuses on the question how mother‟s education exclusively induces child nutrition by providing balance food and maintaining healthy and hygienic environment at household level. The nutritional deprivation between boys and girls by consuming daily calories per day was also looked at. Therefore, major findings regarding child nutrition are given below: The prevalence of stunting or underweight was significantly reduced by increasing daily calorie consumption irrespective of boys or girls. Although, there was a tendency to consume low calorie by a girl per day compared to a boy but the result was not statistically significant (as shown in Appendix Table A21.7). However, in the rural areas, the estimate of underweight exhibited a negative association with daily per capita calorie consumption meaning calorie intake per day deteriorated the status of a child's underweight. This behaviour may be due to seasonal effect of the consumption pattern of the rural people. In rural Bangladesh, calorie consumption fluctuated highly during the peak and off peak season of harvesting (HKI/Bangladesh 1998). In the off peak season, the prevalence of underweight is high while it is low in the peak season. Perhaps, this fluctuation reflected in the estimate indicating child‟s underweight in the rural areas.
242 All levels of mother‟s education except primary significantly improved child nutrition while in the case of father‟s education, only higher secondary education had some positive effects on child nutrition. At the rural level also, father‟s education did not have any significant effect on child nutrition. Moreover, the 'beta coefficient' which measures the relative strength of each predictor also showed that the coefficients of mother‟s various levels of education were stronger than those of father‟s education in improving child nutrition (as shown in Chapter 7). Therefore, mother‟s education undoubtedly contributes to protect the children from malnutrition. In relation to gender, whether a girl child was being more malnourished (either stunted or underweight) compared to a boy - no significant difference between boy and girl was found. Although girls were likely to consume less per day compared to boys, the coefficients were not statistically significant. Thus, the estimated results did not show any systematic girls' deprivation in nutritional status. More than 90 per cent sample population particularly in rural areas used tube well water for drinking which is typically known as safe water (arsenic contamination was not considered). However, only tap water had a positive impact on nutrition compared to the non-safe pond/river water at the national. But at the village level, effects of all sources of drinking water were positive and also highly significant. This behaviour probably due to, in Bangladesh, most rural households generally use drinking water either from shallow tube-well or deep tube-well and thus have positive associations with child nutrition. The use of hygienic toilet by the household members had a significant effect in improving nutrition compared to the households used non-hygienic toilets particularly at the national level. Unexpectedly, the use of non-hygienic toilet did not deteriorate nutritional status particularly among the rural children. However, the results of this variable at the national level were quite logical and consistent in explaining child nutrition. But in the case of washing mother‟s hands, no systematic positive effect was observed rather unusually it deteriorated child nutrition. At the village level also, unexpectedly, the estimates of both stunting and underweight were not only negative but also significant. May be mothers‟ were not very careful in using proper materials for washing hand or may be their reporting was false while data was collected.
243 Distance to health centres from the village was significantly and negatively associated with nutrition at the rural areas both in the case of stunting and underweight. The longer distance to health centres increased the prevalence of malnutrition while conversely, the proximity of health centres to the village declined the prevalence of malnutrition. The impact of distance to health centres on child nutrition is particularly important as the health services are comparatively less available in the rural areas. The analysis reported that daily calorie consumption played a significant role in reducing child malnutrition, although there was exception in the prevalence of underweight at the village level, which might occur due to seasonal factors. In order to counteract the peak and off peak situation regarding calorie consumption, mother‟s education helped to maintain a balance diet particularly for the children. However, nutrition and daily calorie consumption were closely related as it was expected. In the case of parent‟s education, particularly mother, having at least lower secondary education had significant impacts in reducing the prevalence of a child being stunted or underweight. By contrast, father‟s education had very little impact in protecting the child from malnutrition. At the rural level also, mother‟s education had significant effect in improving child nutrition while father‟s education yielded no systematic effect. That for nutrition, unlike school attendance, mother's education had a highly significant differential impact compared to father‟s education. The implication is that educated mothers can enhance the productivity of the future workforce and this would tremendously benefit a country's economic growth. No systematic difference between boy and girl in daily calorie consumption was evident by this study. Only tap water had some impact on child nutrition at the national level while all sources of drinking water had significant impacts at the rural level because most of the sources are the rural based. The use of types of toilet had systematic impact in improving child nutrition particularly at the national level. But washing mother‟s hand unusually it showed negative association which might occur due to false reporting by the mothers. At the village level, distance to health centre deteriorated the nutritional status of rural children. This context encouraged the role of educated mothers to act as a substitute of health worker in improving child nutrition.
244 Although major health indicators showed steady gains in the overall health status of Bangladesh over the years, service delivery in the public health sector is extremely limited, particularly, in the rural areas due to shortage of skilled health personnel and serious lack of awareness among the families particularly mothers to maintain a healthy life for their children. Among South Asian countries, it is appeared that Bangladesh lagged behind India and Pakistan in the case of child nutrition. However, positive impacts of mother‟s education associated with child nutrition from this empirical analysis demonstrated that investing more on girls‟ education needed to be the priority in the country like Bangladesh to improve its long term development potential.
8.4 Policy Implications of this Study
8.4.1 Policy Implications: School Attendance This thesis provides a detail analysis on children‟s level-wise (primary school, secondary school) school attendance and its response to various determinants. Importantly, it shows that both father‟s and mother‟s education of a child are equally important for educational attainment. It also shows that Bangladesh is confronting with wide variation in curriculum, very poor quality education, wide gap between rural and urban children in school performance and low girl‟s participation at tertiary education. Formal schooling (average years of schooling, primary school completion rate and literacy rate) is the principal routes towards the development of knowledge and skills (Sandiford, et al. 1995; P. Schultz 2002; Boyle et al. 2006) and also used as the base of level of human capital. It has thus, great significance in the economic development of a country. For example, the qualitative transition in the labour force equipped with employment contributed significantly to Singaporean economy (M/O Labour, Singapore 1997). Because, in Singapore, the proportion of economically active women in the total labour force rose from 37 per cent in 1986 to 42 per cent in 1996 and the corresponding proportion of women with upper secondary education went from 53 to 66 per cent during the same period. Since, in Bangladesh, drop out rates are higher for girls particularly after grade VI and far, 16.7 per cent girls completed grade X as against 23.5 per cent for boys (BBS & UNICEF 2009). Boys performed better than girls in all types of primary and secondary institutions, irrespective of public or private, urban or rural schools (CAMPE
245 2009), Bangladesh could harness its potential productivity of women themselves as well of the future workforce through higher investment in girls‟ education. This thesis provides the evidence that those parents had at least lower secondary or secondary education (class 1-class X) yielded the highest impacts on children‟s school attendance. In consideration with this findings and the perspective of resource constraint as a low income country, Bangladesh needs to ensure at least lower secondary/ secondary education for all children with special attention towards girls. This thesis helps extensively to formulate appropriate policies and programs in order to enhance the literacy rate for its entire population, the great challenge Bangladesh faces. Some important recommendations are mentionable:
i) invest more on educating girls‟ to increase potential productivity;
ii) at least lower secondary education is essential for effective outcome;
iii) enhance school attendance for all children particularly for girls; and
vi) increase opportunities for women‟s labour force participation.
8.4.2 Policy Implications: Child Nutrition
It is well known that the avenues for investment in human capital comprise calorie consumption, medical care and vitamin consumption along with schooling, training and other skill development activities (Becker 1962). Thus, expenditures for consumption in early years of age significantly contribute to improving physical and mental abilities of people and thereby raise their real income prospects. The variable daily calorie intake implies a compulsion in consuming a certain level of calorie per day particularly for the children, which may not be ensured even in a wealthy family in Bangladesh. Therefore, daily calorie consumption provides an indication how to protect children from stunting or underweight in order to increase the probability of achieving the higher income in later life (Finlay 2006). In a growing economy like Bangladesh, although household income increased over the last few years, but household consumption expenditure was not increased that much (BBS 2007a). This is reflected in the status of child nutrition which is stagnant over the last few years and thus warrants special attention to improve the situation. Moreover, although health interventions reduced child
246 mortality, they did not curtail the deprivation associated with nutrition for girls (Muhuri 1995). Therefore, higher formal education for girls needs to be increased as it is critically important for the promotion of child health, nutrition and disease prevention and girls‟ deprivation in nutrition rather than undertaking expensive health interventions. The unique feature of this thesis is that it showed the individual effects of father‟s and mother‟s education in reducing child malnutrition. The analysis asserted that mother‟s education in Bangladesh had a significant impact in reducing the prevalence of a child being either stunted or underweight. It was also indicated that mothers/girls need to have at least lower secondary eduction to materialise health related knowledge and skills. By contrast, father‟s education had no systematic effect on child nutrition. Thus, for nutrition, mother's education had a significant differential impact compared to father's education. For maintaining a good nutrition level of a child at the early age (less than 6 years), mother‟s education is essentially important. The other determinants of child nutrition such as sources of drinking water, toilet use and washing mother hand are also closely related with mother‟s education. Therefore, the policies directed to improve child nutrition by investing more on girls‟ education are essentially important.
8.5 Contributions of the Study and Areas of Research A common and clear understanding of women‟s contribution to economic development particularly in low income countries through their role played at household level is provided by this thesis. Their specific contributions to protect children from malnutrition and acquiring higher education for them have also been recognised. In Bangladesh, since women‟s labour market participation is very low (on average less than 30 per cent), the direct benefits from women‟s education may not always be ensured, therefore, indirect benefits to improve nutrition and increase learning capability of the children could certainly be attained by the role played at household level. This thesis contributes significantly by pointing out inter-relationships between factors in determining human capital and the process how educated women extensively help to accumulate children‟s human capital at household level and thus recommends policies to improve the cycle of low nutrition and low education for the children. Within the household, parent‟s education is an important determinant which extensively influences the process of children‟s human capital formation (Ermisch &
247 Francesconi 2001) and this thesis supports the proposition that both father‟s and mother‟s education are equally important in enhancing child attendance and also supports the studies those have stressed the characteristics of the family as the important components for human capital formation, where children are born and brought up (Khandaker 1996; Ermicsh & Francesconi 2001). However, no evidence found in this study that mother‟s education increased her daughter‟s school attendance either at primary level or at secondary level. But educated fathers had some impacts on their son‟s school attendance at secondary level. Moreover, although, girls were likely to attend school more than boys‟, it was may be due to the influence of various programs implemented by the governments rather than the influences of parent‟s education. In the case of child nutrition, the study showed that daily calorie consumption significantly improved children‟s nutritional status. In the case of parent's education, particularly mother‟s levels of education had stronger effects in improving child nutrition than those of father‟s education. A mother can cater her child through providing balanced diet, hygienic environment and proper treatment at household level and thus have a strong influence on growing up a strong and healthy child. However, this study underscored the need for a broader and integrated vision for improving child nutrition and also emphasised continuation of ongoing nutrition programs such as supplementary food supply to the pregnant women, nutritious biscuit to the primary school students, awareness raising regarding daily calorie intake, cooking process including special attention to the mother‟s education and price support to the daily necessities particularly food items.
8.5.1 An Econometric Framework for Empirical Analysis
This study has put forward an econometric framework for empirical investigation in social sector problems such as women‟s education, child nutrition etc. This framework is useful in evaluating other policies undertaken for economic development particularly in Bangladesh. Among the determinants of school attendance, household income increase children‟s school attendance at both primary and secondary level significantly. Parent‟s levels of education have also significant influences on children's school attendance. The level wise education shows that particularly mother's education has the systematic and
248 consistently higher impact on children's nutritional improvement as the level of education increases. However, as a low income country, Bangladesh could ensure at least lower secondary education for children because education expansion to the secondary level appeared extremely important for reaping many health and demographic benefits (Hannum & Buchmann 2005). The analysis also showed that at least lower secondary education for parents would yield highest impact within affordable capability of the government. Without participating in the formal labour market, the contribution made by the educated women at household level could increase potential productivity of the labour force and thus generates the expected outcome instead of undertaking a large volume of project for health and nutrition.
8.5.2 First Study in the Bangladeshi Context In the socio-economic context of Bangladesh, several studies particularly in the areas of education and health were conducted. For example, Asadullah (2006, 2009) focused on the rate of returns to education in relation to male and female education, public and private education. The rate of return analysis exposes the rate of returns to workforce provided they must participate in the formal labour market, while a substantial number of women in Bangladesh do not participate in the labour force. In this context, the rate of return analysis may not yield desired result rather being educated these women could contribute extensively at the household level and also working as a driving force to develop the country. However, this is the first comprehensive study on women‟s education with the distinct feature that it persuaded the indirect benefits of women‟s education in relation to children‟s school attendance and their nutritional improvement in a single framework. The findings of high level of human capital accumulation is to accelerate economic growth, by relying on a well-trained, educated and hard-working labour force irrespective of men and women (Becker 1993) even the country lacking natural resources. This strengthens the arguments in favour of increasing investment in girls‟ education by prioritizing the allocation of public resources to both primary and secondary education particularly in Bangladesh.
8.5.3 Methodological Contribution Researchers, particularly in Bangladesh usually face method selection problem while conducting social sector investigation such as women‟s education. As a practical
249 solution to this problem, a sequential procedure for social investigation, based on general and specific approaches, has been developed and used in this study. This procedure helps in determining the appropriate method to handle cross-section data. The policy regarding enhancement of school attendance and nutritional improvement does not necessarily require huge financial involvement, rather policy design would help enormously in the process of accumulating knowledge and skills of the future workforce. In consideration with the socio-economic context of Bangladesh, women‟s labour force participation is not always certain and also due to cultural constraints, women can contribute substantially, particularly the role is played by them at household level.
Although, a fairly standard economic approach is used in this thesis, however, the way to develop the framework is a contribution in the area of social research in Bangladesh. The use of econometric approach in the social cases, it needs some manipulation about the available data and also needs to minimise the limitations in order to have fair and valid results. In education data, a large number of data was missing which might truncate the actual number of literate person. Along with this limitation, the models are estimated and the obtained results are rational and also comparable with other studies such as World Banks‟ MDG Report (2005), Education Watch Report 2002, 2003, NIPORT Report 2004, and the study of Asadullah 2006 and thus added value in the areas of social research.
8.5.4 Suggestions for Future Studies The investigation based on household level data in this thesis provides some insightful findings on how educated women indirectly contribute to the economy through enhancing children‟s educational attainment and their nutritional status. There are some other important issues, which could not be covered by this study. In the diversified area of human capital these issues are: the rate of returns to women‟s education including private rate of returns and social rate of returns, women‟s labour market participation, wage differentials between men and women, women‟s employment opportunities and the opportunities for utilising their potential productivity. In the case of returns to education, although private economic returns to education is dominant, social rate of returns which includes social costs (public spending for
250 education) and social benefits (additional income-tax paid by the individual to the government due to extra income) are also needed to be evaluated further for effective utilisation of public investment in education and health sectors so that common people could be benefited in view of the greatly limited resources of low income countries like Bangladesh.
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269 Appendix A
Table A1.2: Gross Enrolment Ratio: Pacific Island Countries during 1990-2009 Country Primary Secondary Tertiary 1990 2000 2009 1990 2000 2009 1990 2000 2009
Fiji 131 109 105 58 81 86 8.3 - 15.3* Papua New Guinea 66 79 75* 11.5 23 26* 3.0 2.1 - Solomon Islands 86 86 109** 14 19 36** - - - Vanuatu 96 113 110 17 34 49 - 4.0 5.0 East Asia & Pacific 111 72 20 Group Average 2005 Source: World Bank, Country Education Profile 1990, 2000, 2009. * indicates 2005 data and ** indicates 2007 data.
TableA2.2: Mean Years of Schooling in Pacific Island Countries Country Males Females Total
Fiji 5.6 4.6 5.1 Papua New Guinea 1.2 0.6 0.9 Solomon Islands 1.2 0.8 1.0 Vanuatu 4.3 3.1 3.7 All developing countries 4.6 2.7 3.7
Source: Gannicott, KG & Avalos, B 1994, Women's Education and Economic Development in Melanesia, Pacific 2010, National Center for Development Studies (NCDS), Canberra, Australia.
TableA3.3: Major Goals and Targets of Bangladesh Indicators 1990 2002 Annual 2015 Annual Progress over Progress over 1990-02 (%) 2002-15 (%)
Income-poverty (%) 59 50 -1.5 25 -3.3 Extreme-poverty (%) 28 19 -3.2 9.5 -3.3 Adult literacy (%) 35 49.6 3.5 90 6.3 Primary enrolment (%) 56 86.7 4.6 100 1.2 Secondary enrolment (%) 28 52.8 7.4 95 6.1 Infant mortality rate (%) 94 53 -3.6 18 -5.1 Under-5 mortality rate (%) 108 76 -2.5 25 -5.2 Maternal mortality rate (%) 554 390 -2.5 98 -5.8 Life expectancy at birth (year) 56 64.9 1.3 73 1.0 Population growth (%) 2.1 1.4 - 1.3 - Underweight children (5) 67 51 -2.4 26 -3.3 Source: National Strategies for Accelerating Poverty Reduction (NSAPR I) 2005; Note: Benchmark is 2002
270 TableA4.3: Education System in Bangladesh
A. General Education It comprises three levels, viz. Primary, Secondary and Tertiary.
1. Primary education This level is of 5-year duration and it is generally meant for the (Grade I-V) children of the age group of 6-10 years. 2. Secondary Education: This stage is of 7 years with following sub-levels.
i. Junior Secondary This level is of 3-year duration and meant for the children of age group Education (Grade VI- of 11-13 years. VIII) ii. Secondary Education This level is of 3-years duration and meant for the boys and girls (Grade IX-X) belonging to the age group of 14-15 years. At the end of this level, students are to appear at the public examination and the successful candidates are offered the secondary School certificates (S.S.C). iii. Higher Secondary This level is of 2-year duration and meant for the boys and girls (Grade XI-XII) belonging to the age group of 16-17 years. At the end of this level, students are to appear at the public examination and the successful candidates are offered the Higher Secondary certificate (H.S.C). 3. Tertiary education This is of 3-year Pass-Course or 4-year Honours Courses for Bachelor (Grade XIII and degree followed by a 2-year Masters Course for the Pass Graduates above) and 1-year for the Honours graduates. These courses are offered by the Colleges and Universities. At this level faculties are: Arts and Humanity, Science and Commerce. Professional colleges and Universities offered 4-year engineering, medical and agricultural education. There are some others technical colleges also exist: leather technology, textile-engineering. B. Madrasa Education Parallel to the main stream of formal education, there are 5-stages of Madrasa education System (Islamic Education) with 5-year Ebtedayee, 5-year Dakhil, 2-year Alim, 2-year Fazil and 2-year Kamil courses. The Madrasas adopt national curricula as a condition of government recognition and support. Students are required to appear at the Public examination after completing courses of each level, except Ebtedayee.
C. Technical education There are separate streams, which exist for vocational, technical education. After completing junior secondary level (Grade VIII), student enter into Vocational Training Institutes (VTI) for 2-year SSC (Vocational) courses and after having SSC, they enter into VTI, Polytechnic Institutes for 2-year HSC or 3- year Diploma-in- Engineering courses. After passing HSC student enter into professional colleges and universities.
Source: BANBEIS, Ministry of Education, Government of Bangladesh, Dhaka. 271 TableA5.3: Millennium Development Goals and Targets
Millennium Development Goals
1. Eradicate extreme poverty and hunger. 2. Achieve universal primary education. 3. Promote equality between men and women and empower women. 4. Reduce under-five mortality by two-thirds. 5. Reduce maternal mortality by three-fourths. 6. Reverse the spread of communicable diseases. 7. Ensure environmental sustainability. 8. Create a global partnership for development, with targets for aid, trade and debt relief.
Millennium Development Targets 1. Halve by 2015, the proportion of people living below national poverty line. 2. Halve by 2015, the proportion of people who suffer from hunger 3. Ensure that all boys and girls complete a full course of primary schooling 4. Eliminate gender disparity in primary and secondary education preferably by 2005, and at all levels by 2015 5. Reduce by two thirds by 2015, the under-five mortality rate 6. Reduce by the three quarters, by 2015, the maternal mortality ratio 7. Have halted by 2015 and begin to reverse the spread of HIV/AIDS 8. Have halted by 2015 and begin to reverse the incidence of malaria and other major diseases 9. Integrate the principles of sustainable development into country policies and programmes and reverse the loss of environmental resources 10. Halve by 2015, the proportion of people without sustainable access to safe drinking water and sanitation 11. By 2020, have achieved an improvement in the lives of at least 100 million slum dwellers 12. Develop an open, rule-based, predictable, non-discriminatory trading and financial system 13. Address the special needs of the least developed countries 14. Deal comprehensively with the debt problems of developing countries through national and international measures in order to make debt sustainable in the long-term 15. Develop and implement strategies for decent and productive work for youth in consistent with developing countries 16. In cooperation with pharmaceutical companies, provide access to affordable essential drugs in developing countries 17. In co-operation with the private sector, make available new technologies, specially information and communications
Source: Government of Bangladesh (GoB) 2007; Progress in Achieving Millennium Development Goals (MDGs) 2007’, General Economics Division, Planning Commission, Dhaka.
272
TableA6.4: Country Health Profile: Bangladesh Facilities Data (latest) Source and Year Demography Population 162.0 million WDI 2011 Under-15 population (%) 36 NIPORT, 2008 Female population (15-49 years) (%) 53 NIPORT, 2008 Population density per sq.km. 900 BBS, 2006d Population growth rate (%) 1.4 GoB, 2010 Total fertility rate (%) 2.3 GoB 2010 Urban population (%) 25 UNDP, 2005 Life expectancy at birth (years) 67 GoB, 2010 Mean age at first marriage for girls (years) 19 GoB, 2010 Health Status
Infant mortality rate (per 1000 live births) 41 WDI 2011 Under-5 mortality rate (%) 52 WDI 2011 Population of using safe drinking water (%) 80 WDI 2011 Population of using improved sanitation (%) 53 WDI, 2011 Delivery attended by trained personnel 24 UNDP 2010 Number of qualified hysician 51993 Population per physician 2773 DGHS, 2010 Physicians per 10, 000 population 3 DGHS, 2010 Population per nurse 6180 DGHS, 2010 Hospital beds per 10,000 population 4 DGHS, 2010 Total expenditure on Health as % of GDP 3.4 WDI 2011 Public expenditure on health as % of total expenditure on 32 WDI 2011 health Private expenditure as % of total expenditure 71 WHO 2008 Source: Country Health System Profile: Bangladesh, WHO 2008; Health Bulletin 2010, DGHS, MHAFW; World Development Indicator 2011
TableA7.4: Death of Children under5 by Mother’s Education: Bangladesh
Cause of Death Mother’s education
None Primary Secondary complete complete or above Number of death 288 (49%) 180 (31%) 104 (18%) Neonatal tetanus 3.4 2.3 0.0 Congenital abnormality and Injury 6.2 3.5 14.7 Drowning 2.0 1.2 9.0 Birth asphyxia 8.2 17.1 24.5 Measles 0.7 0.5 0.0 Diarrhoea 5.8 5.2 3.8 Acute respiratory infection (ARI) 24.8 23.4 17.1 Possible serious infection* 32.8 29.3 28.7 Malnutrition 8.7 13.4 8.6 Others 9.4 5.4 2.9 Total 100 100 100
*Possible serious infections include possible ARI and diarrhoea
Source: Population Report 2004, BDHS, Ministry of Health, Government of Bangladesh, Dhaka.
273 TableA8.5: Variance Inflation Factor (VIF): Primary School Attendance
Variable VIF 1/VIF
Monthly per capita Expenditure (MPCE) 1.39 0.718 Mother’s Education (ME) Primary level 1.25 0.802 Lower secondary 1.36 0.736 Higher secondary 1.82 0.549 Graduate and above 1.37 0.732 Father’s Education (FE) Primary level 1.16 0.863 Lower secondary 1.22 0.817 Higher secondary 1.71 0.585 Graduate and above 1.79 0.558
Girl’s status in school attendance (Sex2) 1.00 0.999
Age of Child (AC) Year 7 1.02 0.979 Year 8 1.02 0.981 Year 9 1.02 0.984 Year 10 1.02 0.978 Mean VIF 1.28
Source: Author’s calculation based on HIES 2000, BBS Ministry of Planning, Government of Bangladesh.
Table A9.5: Variance Inflation Factor (VIF): Secondary School Attendance Variable VIF 1/VIF
Monthly per capita Expenditure (MPCE) 1.39 0.718 Mother’s Education (ME) Primary level 1.25 0.802 Lower secondary 1.36 0.736 Higher secondary 1.82 0.549 Graduate and above 1.37 0.732 Father’s Education (FE) Primary level 1.16 0.863 Lower secondary 1.22 0.817 Higher secondary 1.71 0.585 Graduate and above 1.79 0.558
Girl’s status in school attendance (Sex2) 1.00 0.999
Age of Child (AC) Year 7 1.02 0.979 Year 8 1.02 0.981 Year 9 1.02 0.984 Year 10 1.02 0.978
Mean VIF 1.28
Source: Author’s calculation from HIES, 2000, BBS Ministry of Planning, Government of Bangladesh.
274 Table A10.5: Variance Inflation Factor (VIF) for HAZ/WAZ Variable VIF 1/VIF
Daily per capita Calorie Consumption (DCC) 1.04 0.128 Mother’s Education (ME) 1.28 0.778 Primary level 1.39 0.721 Lower secondary 1.87 0.534 Higher secondary 1.43 0.699 Graduate and above Father’s Education (FE) 1.19 0.838 Primary level 1.27 0.788 Lower secondary 1.80 0.555 Higher secondary 1.83 0.547 Graduate and above Girl’s status in Nutrition (Sex2) 1.01 0.992
Sources of Drinking Water (Dr) Tube-well (deep) 7.81 0.128 Tube-well (shallow) 3.92 0.255 Tap water 5.01 0.199 Well 1.68 0.594 Types of Toilet (TT) Flush/sanitary/water-sealed 1.56 0.641 Pit latrine (closed) 1.45 0.690 Pit latrine (open) 1.24 0.807 Hanging latrine 1.05 0.955 Open space 1.37 0.730 Washing Mother’s Hand (WH) 1.02 0.976
Mean VIF 2.01 Source: Author’s calculation from HIES, 2000, BBS Ministry of Planning, Government of Bangladesh. Note: Table reporting VIF for HAZ is exactly same for WAZ.
Table A11.6: Level of Education by Age: Bangladesh
Age None Primary Lower Higher Graduate & Others secondary secondary above 0-5 143 125 - - - 1 6-10 421 3742 93 1 - 11 11-15 147 1601 1436 69 2 28 16-25 98 52 195 770 399 10 26-35 8 5 - - 39 3 36-45 2 1 - - - 1 46-55 3 2 - - - - Total 822 5528 1725 840 440 54 Source: Author’s calculation from HIES, 2000, BBS Ministry of Planning, Government of Bangladesh.
275 Table A12.6: Primary School Attendance and Average expenditure of schooling
Independent Variable Margi Std. z P>z x-bar [ 95% C.I. nal Err. ] Effects Household Income .0002 .002 .98 0.33 788.29 -.003 003 Girls’ Status .13 1.09 1.18 0.24 0.48 -2.01 2.26
Average Exp. of Schooling -.001 .004 -9.16 0.00 4170.65 -.008 .007 Father’s Education .75 7.79 0.04 0.97 .140 -14.51 16.01 Primary level .56 3.59 0.14 0.89 .08 -6.47 7.59 Lower secondary .58 4.05 0.11 0.91 .14 -7.37 8.52 Higher secondary .65 14.14 0.02 0.99 .03 -27.06 28.36 Graduate & above
Mother’s Education -.08 .84 -0.51 0.61 .14 -1.74 1.58 Primary level .57 3.01 0.25 0.78 .07 -5.34 6.48 Lower secondary .72 11.22 0.01 0.99 .07 -21.28 22.72 Higher secondary .67 9.35 0.01 0.99 .01 -17.65 18.99 Graduate & above
Father’s Education* Boy Boy* Primary level -.39 8.47 -0.04 0.97 .07 -17.00 16.21 Boy* Lower secondary .09 5.18 0.02 0.98 .05 -10.06 10.24 Boy* Higher secondary .60 32.8 0.01 0.99 .07 -63.69 64.90 Boy* Graduate & above .32 153.5 0.00 0.99 .02 -300.58 301.22
Mother’s Education*Girl Girl*Primary level .77 6.36 0.47 0.64 .065 -11.70 13.25 Girl*Lower secondary .55 88.93 0.00 0.99 .038 -173.75 174.85 Girl*Higher secondary -.13 78.33 -0.00 0.99 .033 -153.66 153.39 Girl*Graduate & above -.34 7.46 -0.00 0.99 .004 -14.97 14.29
Age of Child Year 7 .22 1.17 1.56 0.12 .22 -2.09 2.53 Year 8 .18 1.04 1.40 0.16 .20 -1.86 2.22 Year 9 .80 4.76 2.70 0.01 .16 -8.54 10.14 1.98 Year 10 .27 1.31 0.05 .24 -2.29 2.84
Number of obs. = 5651 LR chi2(23) = 7115.84 Prob > chi2 = 0.0000 Log likelihood = -57.56 Pseudo R2 = 0.9841
obs. P .662 pred. P .336 (at x-bar) (*) dF/dx is for discrete change of dummy variable from 0 to 1 z and P>z correspond to the test of the underlying coefficient being 0
Source: Authors calculation based on HIES 2000, BBS, Ministry of Planning, Government of Bangladesh.
276 Table A13.6: Household Income and Primary School Attendance
Income Group Coefficient Standard Error Poorest -.16 .0202 Poorer -.06 .0201 Middle-income group (ref. group) - - Richer .05 .0197 Richest .10 .0196
Number of obs. = 5651 F( 4, 5646) = 51.71 Prob > F = 0.000 R-squared = 0.033; Adj R-squared = 0.035
Source: Authors calculation based on HIES 2000, BBS, Ministry of Planning, Government of Bangladesh.
TableA14.6: Household Income and Secondary School Attendance Income Group Coefficient Standard Error Poorest -.19 .0196 Poorer -.09 .0199 Middle-income group (ref. group) - - Richer .15 .0194 Richest .31 .0180 Number of obs. = 5651 F( 4, 5649) = 51.71 Prob > F = 0.000 R-squared = 0.035; Adj R-squared = 0.035
Source: Authors calculation based on HIES 2000, BBS, Ministry of Planning, Government of Bangladesh.
TableA15.6: Primary School Attendance with Interaction Variables Only
Independent Variable Merg. Std. t P>t x bar [95% C. I.] Effect Err. Father’s Edu*Boy (FE*b) Boy* Primary level .12 .02 4.90 0.000 .068 .075 .160 Boy* Lower secondary .12 .03 4.27 0.000 .047 .071 .171 Boy* Higher secondary .17 .02 7.36 0.000 .069 .135 .212 Boy* Graduate & above .16 .04 3.49 0.000 .017 .084 .230
Mother’s Edu*Girl
(ME*g) Girl* Primary level .18 .02 7.37 0.000 .064 .141 .219 Girl* Lower secondary .17 .03 5.49 0.000 .038 .119 .218 Girl* Higher secondary .16 .03 4.88 0.000 .033 .107 .215 Girl* Graduate and above .002 .10 0.03 0.980 .004 -.186 .191
LR chi2(8) = 170.33 Prob > chi2 = 0.0000 Pseudo R2 = 0.0236 Number of obs = 5651 obs. P .66 pred. P .67 (at x-bar)
(*) dF/dx is for discrete change of dummy variable from 0 to 1 z and P>z correspond to the test of the underlying coefficient being 0 Probit regression, reporting marginal effects
Source: Authors calculation based on HIES 2000, BBS, Ministry of Planning, Government of Bangladesh 277
TableA16.6: Primary School Attendance without Interaction Variables Independent Marg. Std z P>z x-bar [95% C. I.] Variable Effect Err.
Household Income .0001 .00001 4.92 0.000 788.29 .00004 .0001
Father’s Edu. (FE) 0.000 .139 .085 .153 Primary level .12 .017 6.26 0.000 .084 .063 .151 Lower secondary .12 .023 4.33 0.000 .135 .083 .167 Higher secondary .13 .021 5.29 0.215 .035 -.029 .144 Graduate and above .06 .044 1.24
Mother’s Edu.(ME) .019 0.000 Primary level .12 5.65 .139 .079 .152 .027 0.002 Lower secondary .09 3.03 .075 .034 .139 .032 0.024 Higher secondary .08 2.25 .067 .014 .141 -.16 .096 -1.78 0.075 Graduate and above .008 -.352 .024
Age of Child -.51 .021 -20.69 0.000 .183 -.549 -.466 Year 7 -.25 .024 -10.50 0.000 .222 -.300 -.205 Year 8 -.08 .025 -3.44 0.001 .198 -.135 -.035 Year 9 -.15 .024 -6.23 0.000 .242 -.194 -.099 Year 10 .013 0.040 .484 .001 .052 Sex2 .03 2.06
LR chi2(14) = 893.27 Prob > chi2 = 0.0000 Pseudo R2 = 0.1235
F ratio = (.124-.024)/8/ (1-.124)/5638 =83.33> F-critical value Number of observation = 5651 obs. P .66 pred. P .68 (at x-bar)
(*) dF/dx is for discrete change of dummy variable from 0 to 1 z and P>z correspond to the test of the underlying coefficient being 0 Probit regression, reporting marginal effects Source: Authors calculation based on HIES 2000, BBS, Ministry of Planning, Government of Bangladesh
TableA17.7: Parent’s Education and HAZ
HAZ Coef. Std. Err. t P>t [95% C. I.]
Father’s Education (FE) Primary level .10 .070 1.44 0.149 -.036 .239 Lower secondary .08 .088 0.86 0.388 -.097 .250 Higher secondary .32 .085 3.72 0.000 .151 .486 Graduate and above .51 .165 3.09 0.002 .185 .833 Mother’s Education (ME) Primary level .14 .075 1.80 0.072 -.012 .282 Lower secondary .30 .089 3.35 0.001 .124 .474 Higher secondary .64 .104 6.12 0.000 .436 .847 Graduate and above 1.25 .266 4.71 0.000 .734 1.77
Constant -2.09 .026 -79.76 0.000 -2.14 -2.04 F( 8, 3991) = 28.58 Prob > F = 0.0000 R-squared = 0.054 Adj R-squared = 0.052; Number of obs. = 4000
Source: Authors calculation based on HIES 2000, BBS, Ministry of Planning, Government of Bangladesh. 278 TableA18.7: Parents Education and WAZ
WAZ Coef. Std. Err. t P>t [95% C. I.]
Father’s Education (FE) Primary level .09 .050 1.83 0.067 -.006 .191 Lower secondary .04 .063 0.61 0.541 -.086 .164 Higher secondary .20 .061 3.29 0.001 .082 .323 Graduate and above .27 .118 2.32 0.020 .042 .507
Mother’s Education (ME) Primary level .08 .054 1.48 0.140 -.026 .185 Lower secondary .24 .064 3.72 0.000 .113 .364 Higher secondary .48 .075 6.44 0.000 .337 .633 Graduate and above .78 .191 4.06 0.000 .401 1.151
Constant -2.08 .018 -110.65 0.000 -2.12 -2.04
F( 8, 3991) = 25.59 Prob > F = 0.0000 R-squared = 0.0488 Adj R-squared = 0.0469
Number of observation = 4000
Source: Authors calculation based on HIES 2000, BBS, Ministry of Planning, Government of Bangladesh.
TableA19.7: Stunting (HAZ) with Beta Coefficient
Stunting (HAZ) Coef. (raw) Std. Err. t P>t Beta
Father’s Education (FE) Primary level .10 .070 1.44 0.15 .024 Lower secondary .08 .089 0.86 0.39 .015 Higher secondary .32 .086 3.72 0.00 .076 Graduate and above .51 .165 3.09 0.002 .063 Mother’s Education (ME) Primary level .14 .075 1.80 0.07 .031 Lower secondary .30 .089 3.35 0.00 .060 Higher secondary .64 .105 6.12 0.00 .126 Graduate and above 1.26 .267 4.71 0.00 .086
Constant -2.09 .026 -79.76 0.00 -
Number of observation = 4000 F( 8, 3991) = 28.58 Prob > F = 0.0000 R-squared = 0.0542 Adj R-squared = 0.0523
Source: Author’s calculation from HIES, 2000, BBS Ministry of Planning, Government of Bangladesh.
279 Table.A20.7: Underweight with Beta Coefficient Underweight (WAZ) Coef (raw) Std. Err. t P>t Beta
Father’s Education (FE) Primary level .09 .051 1.83 0.067 .03 Lower secondary .04 .064 0.61 0.541 .01 Higher secondary .20 .061 3.29 0.001 .07 Graduate and above .27 .118 2.32 0.020 .05 Mother’s Education (ME) Primary level .08 .054 1.48 0.140 .03 Lower secondary .24 .064 3.72 0.000 .07 Higher secondary .48 .075 6.44 0.000 .13 Graduate and above .78 .191 4.06 0.000 .07
Constant -2.08 .019 -110.65 0.000 -
F( 8, 3991) = 25.59 Prob > F = 0.0000 R-squared = 0.0488 Adj R-squared = 0.0469 Number of obs. = 4000
Source: Author’s calculation from HIES, 2000, BBS Ministry of Planning, Government of Bangladesh.
Table A21.7: Daily Calorie Consumption and Stunting
Std. [95% C. I.] HAZ Coef Err. t P>t Daily Calorie Intake .0003 .001 6.66 0.000 .0002 .0004 sex2 -.019 .043 -0.45 0.653 -.103 .061 cons -2.52 .096 -26.08 0.00 -2.72 2.33
Number of observation = 4000 F(2, 3997) = 22.34 Prob > F = 0.000 R-squared = 0.011 Adj R-squared = 0.011
Source: Author’s calculation from HIES, 2000, BBS Ministry of Planning, Government of Bangladesh.
Table A22.7: Sources of Water and Stunting
Water Source Coef. Robust t P>t [95% C. I.] S. Err. Safe Water .296 .165 1.79 0.07 -.028 -.619 Non-safe Water ------cons -2.21 .163 -13.52 0.00 -2.53 - 1.89
Number of observation = 4000 F(1, 3997) = 3.21 Prob > F = 0.073 R-squared = 0.0008 Adj R-squared = 0.0006
Note: Non-safe water is reference source
Source: Author’s calculation from HIES, 2000, BBS Ministry of Planning, Government of Bangladesh.
280 Table A23.7: Sources of Water and Underweight Water Source Coef. Robust t P>t 95% Conf. Interval Std. Err. Safe Water .26 .1181 2.19 0.029 .027 .489 Non-safe Water ------constant -2.24 .1171 -18.94 0.000 -2.44 - 1.989
Number of observation = 4000 F( 1, 3998) = 4.78 Prob > F = 0.0289 R-squared = 0.0012 Adj R-squared = 0.0009
Note: Non-safe water is reference source
Source: Author’s calculation from HIES, 2000, BBS Ministry of Planning, Government of Bangladesh.
Table A24.7: Types Toilets and Stunting Types of Toilet Coef. Robust t P>t 95% Conf. Interval Std. Err. Hygienic Toilet .51* .044 11.52 0.000 .420 .592 Non-hygienic Toilet ------Constant -2.11 .026 -79.37 0.000 -2.16 -2.05
Number of observation = 4000 F( 1, 3998) = 132.65 Prob > F = 0.0000 R-squared = 0.0321 Adj R-squared = 0.0318
Note: Non-hygienic Toilet is reference toilet
Source: Author’s calculation from HIES, 2000, BBS Ministry of Planning, Government of Bangladesh.
TableA25.7: Types of Toilet and Underweight Types of Toilet Coef. Robust t P>t 95% Conf. Interval Std. Err. Hygienic Toilet .401*** .031 12.80 0.000 .339 .4623 Non hygienic ------_cons -2.11 .019 -111.56 0.000 -2.148 -2.074
Number of observation = 4000 F( 1, 3998) = 163.79 Prob > F = 0.0000 R-squared = 0.0393 Adj R-squared = 0.0391
Note: Non-hygienic Toilet is reference toilet
Source: Author’s calculation from HIES, 2000, BBS Ministry of Planning, Government of Bangladesh.
281 Annexure B5 Correlation Table B5.1: Primary school Attendance
P.mary MPCE Sex2 AE FE2 FE3 FE4 EE5 ME2 ME3 ME4 ME5 Year 7 Year 8 Year9 Year10
P.mary 1
MPCE .13 1
Sex2 .03 .007 1
AE -.99 -.13 -.02 1
FE2 .08 .003 .023 -0.07 1
FE3 .06 .06 -.03 -.057 -.12 1
FE4 .12 .25 .003 -.11 -.16 -.12 1
FE5 .03 .33 .004 -.03 -.08 -.06 -.08 1
ME2 .10 .06 -.02 -.10 .14 .16 .13 -.05 1
ME3 .07 .16 .02 -.07 -.002 .16 .24 .07 -.11 1
ME4 .08 .35 .006 -0.07 -.092 -.02 .33 .35 -.11 -.08 1
ME5 -.003 .29 .005 .005 -.04 -.03 -.006 .41 -.04 -.02 -.02 1
Year 7 -.04 -.03 .02 .04 -.001 -.007 -.002 -.006 -.005 -.005 -.02 .0001 1
Year 8 .11 -.001 .007 -.12 .014 .001 -.018 .003 -.006 -.008 .007 -.02 -0.27 1
Year 9 .16 .03 .004 -.16 .02 .003 .04 -.001 .01 .029 .006 .02 -0.23 -0.21 1
Year 10 .07 .02 -.03 -.07 -.02 -.008 -.005 .002 -.02 -.04 -.006 .001 -0.30 -0.28 -0.24 1
Note: For convenience, interaction variables are not included in this Correlation Table. The variable female headed household is not included.
282 Correlation Table B5.2: Secondary School Attendance
S.dary MPCE Fhhh Sex2 AE FE2 FE3 FE4 FE5 ME2 ME3 ME4 ME5 Y 11 Y 12 Y 13 Y 14 Y 15 Y 16 Y 17
Sdary 1
MPCE .26 1
Fhhh .02 .01 1
Sex2 .12 .04 .05 1
AE .39 .11 -.01 -.02 1
FE2 -.001 -.02 .02 .001 -.06 1
FE3 .09 -.001 .02 .02 .02 -.12 1
FE4 .25 .19 .02 .04 .08 -.17 -.13 1
FE5 .16 .42 .01 .03 0.09 -.09 -.07 -.11 1
ME2 .14 .03 .02 .02 -.01 .16 .14 .14 -.04 1
ME3 .18 .12 .02 .01 .06 -.01 .16 .27 .04 -.11 1
ME4 .19 .35 .02 .03 .08 -.01 -.05 .29 .43 -.12 -.09 1
ME5 .01 .31 .01 .02 .02 -.04 -.03 -.01 .34 -.04 -.03 -.03 1
Y 11 -.14 -.05 .02 .02 -.3 .02 .004 -.01 -.02 .02 -.003 .003 -.01 1
Y 12 -.08 -.06 .01 .03 -.21 .01 -.001 -.03 -.03 -.001 -.03 -.03 .002 -.20 1
Y 13 .05 -.01 .01 .02 .01 -.01 -.01 -.001 .03 -.01 .02 .02 .02 -.15 -.21 1
Y 14 .10 .01 .01 .04 .10 .001 .03 .02 -.02 .03 .01 -.02 -.002 -.17 -.22 -.17 1
Y 15 .03 .02 .003 -.04 .16 .001 -.03 -.01 .02 -.02 .003 -.01 .00 -.16 -.21 -.16 -.18 1
Y 16 .05 .04 -.04 -.04 .17 -.01 -.002 .03 .01 .001 -.01 .03 -.01 -.15 -.21 -.16 -.17 - .16 1 Y 17 -.01 .08 -.03 -.05 .14 -.02 .01 .01 .03 -.02 .03 .02 .004 -.11 -.15 -.12 -.13 -.12 -.12 1
Note: For convenience, interaction variables are not included in this Correlation Table.
283
Correlation Table B5.3: HAZ (Stunting)
HAZ DCC FE1 FE2 FE3 FE4 fE5 ME1 ME2 ME3 ME4 ME5 Sex2 Dr1 Dr2 Dr3 Dr4 Dr5 T1 T2 T3 T4 T5 T6 WH
HAZ 1
DCC .10 1
FE1 -.15 -.13 1
FE2 .01 .05 -.50 1
FE3 .02 .05 -.39 -.10 1
FE4 .13 .08 -.51 -.13 -.11 1 FE5 .15 .03 -.24 -.06 -.05 -.06 1
ME1 -.18 -.13 .63 -.13 -.24 -.46 -.27 1
ME2 .02 .06 -.29 .19 .20 .10 -.05 -.57 1
ME3 .07 .06 -.31 .06 .15 .26 .01 -.48 -.11 1
ME4 .17 .10 -.36 -.06 .03 .39 .33 -.46 -.10 -.09 1
ME5 .11 .03 -.12 -.03 -.03 -.002 .46 -.15 -.03 -.03 -.03 1
Sex2 -.01 -.02 -.01 -.001 .02 .04 -.03 .02 .003 -.04 .03 -.04 1
Dr1 -.03 .002 .03 -.03 .004 -.02 -.11 .07 -.01 .02 -.09 -.07 -.01 1
Dr2 -.04 .04 .05 -.03 -.03 -.02 .02 .01 -.01 -.02 .02 .002 .03 -.65 1
Dr3 .01 -.04 -.13 -.004 .03 .08 .16 -.13 .03 .003 .14 .12 -.01 -.58 -.08 1
Dr4 -.002 -.02 .02 .01 .01 -.03 .01 .03 -.04 .01 -.01 -.01 .01 -.22 -.03 -.03 1
Dr5 -.03 .01 .04 .02 -.02 -.10 -.02 .01 .02 -.03 -.01 -.01 -.01 -.26 .04 .03 -.01 1
T1 .20 .02 -.21 .03 .003 -.19 .29 -.24 -.01 .09 .26 .17 .001 -.15 .02 .24 -.01 -.04 1
T2 .05 -.01 -.13 .04 .08 .08 .003 -.15 .07 .09 .08 .001 -.003 -.02 .03 .01 -.02 -.002 -.21 1
T3 .01 .004 -.03 .04 .01 .01 -.02 -.02 .03 .01 -.01 -.02 -.01 -.04 -.02 .10 -.03 -.01 -.12 -.20 1
T4 .11 .03 .12 .001 -.02 -.10 -.11 .14 -.01 -.06 -.13 -.06 -.002 .03 .07 -.13 -.04 .02 -.25 -.40 -.24 1
T5 -.02 -.04 .05 -.04 -.02 -.006 -.03 .06 -.01 -.04 -.04 -01 .02 .06 -.03 -.04 -.01 -.02 -.05 -.08 -.05 -.10 1
T6 -.08 -.04 .18 -.04 -.06 -.13 -.08 .19 -.07 -.10 -.12 -.04 .01 .12 -.11 -.12 .12 .03 -.17 -.28 -.16 -.33 .07 1
WH -.01 .04 -.04 .01 .02 .01 .01 -.03 .01 .02 .03 .01 .004 -.02 .03 .01 -.05 .01 .01 .04 .03 .04 .01 -.13 1
Note: For convenience distance from health centres is not included in this Correlation Table. 284 [ Correlation Table B5.4: on WAZ (Underweight)
WAZ DCC FE1 FE2 ME3 FE4 FE5 ME1 ME2 ME3 ME4 ME5 Sex2 Dr1 Dr2 Dr3 Dr4 Dr5 T1 T2 T3 T4 T5 T6 WH
WAZ 1 DCC .10 1
FE1 -.15 -.13 1
FE2 .02 .05 -.50 1 FE3 .01 .05 -.39 -.10 1
FE4 .13 .08 -.51 -.13 -.11 1
FE5 .12 .03 -.24 -.06 -.05 -.06 1
ME1 -.17 -.13 .63 -.13 -.24 -.46 -.27 1
ME2 .01 .06 -.29 .19 .20 .10 -.04 -.57 1 ME3 .07 .03 -.31 .06 .15 .26 .01 -.48 -.11 1 ME4 .16 .10 -.36 -.06 .03 .39 .33 - .46 -.10 -.09 1 ME5 .09 .03 -.12 -.03 -.03 - .46 -.15 -.03 -.03 -.03 1 .002 Sex2 -.01 -.02 -.01 -.001 .02 .02 -.03 .02 .003 -.04 .02 -.04 1
Dr1 -.04 .002 .03 .03 .004 -.03 -.11 .07 -.01 .02 -.09 -.07 -.01 1 Dr2 -.002 .03 .05 -.03 -.03 -.02 .02 .01 -.01 -.02 .02 .002 .03 -.65 1
Dr3 .09 -.04 - -.004 .03 .08 .16 -.13 .03 .003 .14 .12 -.01 -.58 -.08 1 .13 Dr4 -.01 -.02 .02 -.01 .01 -.03 .01 .03 -.04 .008 -.01 -.01 -.26 -.04 -.03 -.01 1 Dr5 -04 .01 .04 -.02 -.02 -.01 -.02 .01 .02 -.03 -.01 -.01 -.01 -.26 -.04 -.03 -.01 1 T1 .21 .02 -.21 -.03 .00 .19 .29 -.24 -.01 .09 .26 .17 .001 -.15 .02 .24 -.01 -.04 1 3 T2 .07 -.01 - .04 .08 .08 .003 -.15 .07 .09 .08 .001 - -.02 .03 .01 -.02 - -.21 1 .13 .003 .002 T3 .02 .004 -.03 .04 .01 .01 -.02 -.02 .03 .01 -.01 -.02 -.01 -.04 -.02 .10 -.03 .01 -.12 .20 1 T4 -.10 .03 .12 .001 -.02 -.10 -.11 .14 -.10 -.06 -.13 -.06 - .04 .07 -.13 -.04 .02 -.23 -.40 -.24 1 .002 T5 -.05 -.001 .05 .04 -.02 -.01 -.03 .06 -.01 -.04 -.04 -.01 .02 .06 -.03 -.04 -.01 -.02 -.05 -.08 -.05 -10 1 T6 -.10 -.04 .18 -.04 -.06 -.13 -.08 .19 -.07 -.10 -.12 -.04 .007 .12 -.11 -.12 .16 .03 -.17 -.28 -.16 -.38 -.10 1 WH .01 .04 .01 .03 .01 .01 .01 .02 .03 .01 .02 .03 .001 .004 -.02 .03 .01 -.05 .01 .04 .03 .04 .01 -.13 1
Note: For convenience, distance from health centres is not included in this Correlation Table.
285 Appendix C
Figures
Figure CF1.6: Distribution of Income (MPCE) by Rural and Urban Areas
Distribution of Monthly per Capita Expenditure, by Sector
rural urban
8.0e-04
6.0e-04
Density
4.0e-04
2.0e-04 0
0 20000 40000 60000 0 20000 40000 60000 monthly per capita exopenditure source: author's calculations from HIES 2000/1 note: distribution, mpce1 (see text for definition)
Figure CF2.6: Distribution of log (Income) by Rural and Urban Areas
Distribution of (log) Monthly per Capita Expenditure, by Sector
rural urban
.8
.6
.4
Density
.2 0
4 6 8 10 4 6 8 10 log(monthly per capita exopenditure) source: author's calculations f rom HIES 2000/1 note: log distribution, mpce1 (see text f or definition)
286 Appendix D
Definitions Various terminologies and concepts used throughout this thesis need to be defined in accordance with their specific applications to the research questions. These are described in the following.
School Attendance (primary level): Primary school attendance is defined as attending classes one to five (I-V) by the children aged 6-10 years. The official age group for primary school is being followed in Bangladesh is 6-10 years. Primary school attendance is used as dependent variable in estimating the effect of maternal education on children’s educational attainment.
School Attendance (secondary level): Secondary school attendance corresponds to the classes VI to XII for children aged 11 to17 years. The official age for secondary school is 11 to 15 years. To capture the higher number of students at the secondary level, in this thesis, secondary school age is considered for 11 to17 years. This is because most children in Bangladesh take 1 to 2 years more to complete their 5-year primary school cycle and therefore may enter secondary school at an older age than officially recognised (CAMPE). Similar to primary school attendance, secondary school attendance is also used as dependent variable to estimate effect of maternal education on children’s educational attainment.
Household (HH): Household consists of a group of persons living together and taking food from same kitchen. The term household and dwelling household are used synonymously (BBS 2007a).
Labour Force/ Economically Active Population: Economically active population or labour force is defined as persons aged 15 to 65 years who are either employed or unemployed during the reference period of the survey (preceding week of the day of survey enumeration). It includes employers, own account workers/self employed persons/
287 commissioned agents, employees and salaried employers, wage earners, paid family workers, members of producers’ co-operative and the persons not classifiable by status (BBS 2004).
Female: The term ‘female’ indicates a common meaning for gender irrespective of age. Women and female are used synonymously in this thesis.
Girl: The term ‘girl’ is defined as those of female gender who belong to the age group 0-17 years. Throughout the thesis, the term ‘girl’ is used when education investment is discussed.
Woman: Woman refers throughout the thesis to those of female gender who are aged 18 years and above.
Adult Literacy Rate (15 + years): The adult literacy rate is the percentage of literate person aged 15 years and above divided by the total population aged 15 years and above and this is multiplied by one hundred.
Primary level: Officially primary level of education is considered as classes I - V. Primary age group: Official age for primary school is considered as 6-10 years.
Net Primary Enrolment Rate: Net Enrolment Rate refers to the number of pupils in the official school age group 6 to 10 years, in a given school year, expressed as percentage of the corresponding population of eligible official age group (GoB 2009).
Number of Children aged 6-10 Currently enrolled in any type of school Net enrolment rate: = x 100 (6-10 year) Total number of children aged 6-10 years
Gross Primary Enrolment Rate: Gross enrolment rate is defined as the number of children enrolled in a particular level of education, regardless of age, as a percentage of
288 the population in the age group associated with that level. For example gross enrolment rate at primary level (class I to V) for age group 6-10 years is given as follows.
Total number of children currently enrolled At primary level (class I to V) Gross enrolment rate: = x 100 (primary level) Total number of children aged 6-10 year
Dropout Rate: Percentage of students of a certain grade, who dropped out during a certain year as a proportion of the students registered in the same class during the year.
Completion Rate (primary level): The completion rate is the percentage of students completing 5-year cycle of primary education among the students enrolled in classes one to five (I-V).
Repetition rate: Percentage of students repeating a certain class among the students registered in the same class during last year.
Secondary Level: Officially secondary school level is considered as classes VI- X. Secondary age group: Aged 11-15 years were considered as secondary age group. Completion Rate (secondary level): The completion rate is the percentage of students completing 10 year cycle of secondary education among the students enrolled in classes six to twelve (VI-XII).
Literacy rate (7 + years): The literacy rate is the percentage of literate person aged 7 years and above divided by the total population aged 7 years and above into hundred.
Number of literate person aged 7 years and above Literacy rate: = x 100 Total population aged 7 years and above
289 Adult Literacy Rate (15 + years) : The literacy rate is the percentage of literate person aged 15 years and above divided by the total population aged 15 years and above into hundred.
Number of literate person aged 15 and above Literacy rate: = x 100 Total population aged 15 years and above
Body Mass Index (BMI): Body mass index (BMI) is a measure of body fat based on height and weight that applies to both adult men and women. BMI Categories: Underweight = <18.5 Normal Weight = 18.5 - 24.9 Overweight = 25 - 29.9 Obesity = BMI of 30 or great MUAC: Mid upper arm circumference (MUAC) is a measurement of the circumference of the arm at a midpoint between the tip of the acromial process of the scapula and the olecranon process of the ulna. It is an indication of upper arm muscle wasting.
National Centre for Health Statistics (NCHS): The Child Nutrition Survey 2000 is conducted on the sample population of ‘Household Income and Expenditure Survey, 2000’ on those families have at least on child was in the age of 6-71 months. This survey basically follows the National Centre for Health Statistics (NCHS), (USA)/WHO, USA standard in calculating height-for-age z-score (HAZ) and weight-for-age z-score (WAZ). This data set has been most widely used internationally as a reference standard for children’s anthropometry. This was formulated in 1975 by the National Centre for Health Statistics/ Centres for Disease Control in the USA by combining growth data from four US sources which is known as NCHS/WHO reference. If, HAZ is less than 2 standard deviation (HAZ <-2SD) meaning moderate stunting while haz <-3SD indicates severe stunting. Underweight is also considered in a similar way that is waz <- 2SD and waz < - 3SD indicate moderate and severe underweight respectively. In 1978 WHO adopted this
290 stunting. Underweight is also considered in a similar way that is waz <- 2SD and waz < - 3SD indicate moderate and severe underweight respectively. In 1978 WHO adopted this NCHS/CDC dataset as the international reference standard for children’s anthropometry. The reference has subsequently been known as the NCHS/WHO reference.
Stunting (HAZ): Stunting indicates reduced linear growth (height or length) compared to the expected growth in a child of same age. Stunting is usually the end-result of chronic and inadequate nutrition, which may show future complications and finally impair the working capacity. This also indicates a long term effect on child health (BBS 2002b). According to the BBS, the formula is given as flows (BBS 200b).
Observed value- Median of Reference Value HAZ Score = (Median of Reference Value – 5th Centile of Ref. Value)/1.8
Underweight (WAZ): Underweight indicates a deficit in body weight compared to the expected weight for the same age, which may result either from a failure in growth or loss of body weight due to infections. Underweight is usually treated as short term phenomenon in expressing malnutrition (BBS 2002b). According to the BBS, the formula is given as flows (BBS 200b).
Observed value- Median of Reference Value WAZ Score = (Median of Reference Value – 5th Centile of Ref. Value)/1.8
291 Appendix E Abbreviations
ABCN = Area Based Community Nutrition ADB = Asian Development Bank ADP = Annual Development Programme AIDS = Acute Immune Deficiency Virus BANBEIS = Bangladesh Bureau of Educational Information and Statistics BBS = Bangladesh Bureau of Statistics BCom = Bachelor of Commerce BDHS = Bangladesh Demographic and Health Survey BEd = Bachelor of Education BEHRWC = Basic Education for Hard to Reach Working Children BGMEA = Bangladesh Garments Manufacturers and Exporters Association BHW = Bangladesh Health Watch BISE = Board of Intermediate and Secondary Education BMEB = Bangladesh Madrasa Education Board BMI = Body Mass Index BOU = Bangladesh Open University BRAC = Bangladesh Rural Advancement Committee BSc = Bachelor of Science BSS = Bachelor of Social Science BTEB = Bangladesh Technical Education Board CAMPE = Campaign for Popular Education CDF = Cumulative Distribution Function CLRM = Classical Linear Regression Model cm = Centi Metre CMR = Child Mortality Rate CNS = Child Nutrition Survey CMNS = Child and Mothers Nutrition Survey CPD = Centre for Policy Dialogue
292 CPR = Contraceptive Prevalence Rate DGFD = Director General of Family Planning Department EFA = Education for All EPI = Expanded Program for Immunisation EOC = Emergency Obstetric Care FHH = Female Headed Household FFW = Food For Work FSSP = Female Secondary Stipend Program FWA = Family Welfare Assistant GDP = Gross Domestic Product GER = Gross Enrolment Rate GNP = Gross National product GO = Government Organisation GoB = Government of Bangladesh GPI = Gender Parity Index HAs = Health Assistants HAZ = Height-for-age z- score HDI = Human Development Index HDR = Human Development Report HH = Household Head HIES = Household Income and Expenditure Survey HIV = Human Immune Virus HKI = Helen Keller International, Bangladesh HPSP = Health and Population Sector Program HQ = Head Quarter HSC = Higher Secondary Certificate ICMH = Institute of Child and Mother Health ICPD International Conference on Population and Development IDCH = Institute of Diseases of Chest and Hospital IFS = Integrated Food Security IMCI = Integrated Management of Childhood Illness
293 IMPS Integrated Multipurpose Sample IMR = Infant Mortality Rate IYCF = Infant and Young Child Feeding Kg = Kilogram LDCs = Least Developed Countries LBW = Low Birth Weight LPM = Linear Probability Model LFS = Labour Force Survey LFP(r) = Labour Force Participation (rate) NPC = National Population Council NSAPR = National Strategy for Accelerating Poverty Reduction MA = Masters of Arts MCH = Medical College Hospital MCWC = Mother and Child Welfare Centres MDGs = Millennium Development Goals MEd = Masters of Education MHH = Male Headed Household MMR = Maternal Mortality Rate MPCE = Monthly per Capita Expenditure MPCI = Monthly per Capita Income MUAC = Mid Upper Arm Circumference NCHS = National Centre for Health Statistics NDP = National Drug Policy NFE = Non-formal Education NGO = Non-governmental Organisation NIPORT = The National Institute of Population Research and Training NNP = National Nutrition Program NPC = National Population Council NRR = Net Replacement Rate NSP = The National Surveillance Project OECD = Organisation for Economic Co-operation and Development
294 OLS = Ordinary Least Square PEDP = Primary Education Development Program PEM = Protein-energy Malnutrition PFDS = Public Food Distribution System Ph.D = Dr of Philosophy PLCEHD = Post literacy PRSP = Poverty Reduction Strategy Paper PSU = Primary Sampling Unit RHS Right Hand Side ROSC = Reaching Out-of School Children SBA = Skilled Birth Attendant SESIP = Secondary Education Improvement Program SFP = School Feeding Program SMA = Statistical Metropolitan Area SMA = Statistical Metropolitan Area SME = Small and Medium Enterprises SSC = Secondary School Certificate SSE = Secondary School Enrolment SSNPs = Social Safety Net Programs STD = Sexually Transmitted Diseases TB = Tuber-clausis Bacillus TFR = Total Fertility Rate TLM = Total Literacy Movement TTC = Teachers Training Centre TVET = Technical and Vocational Education and Training USC = Union health Sub-Centre UHFWC = Union Health and Family Welfare Centre USAID = United States Aid for International Development UNDP = United Nations Development Program UNESCO = United Nations Educational, Scientific and Cultural Organization
295 UNICEF = United Nations Children’s Emergency Fund VGD = Vulnerable Group Development VTI = Vocational Training Institute WAZ = Weight-for-age z-score WB = Word Bank WDIs = World Development Indicators WHO = World Health Organisation WHZ = Weight-for-height z-score
296